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annotate analysis/AnalysisOutput.txt @ 37:d9a9a6b93026 tip

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Add README
author DaveM
date Sat, 01 Apr 2017 17:03:14 +0100
parents 6155f4e3d37c
children
rev   line source
DaveM@34 1 save('AdobeAllResults.mat')
DaveM@34 2 analysisWorkflow
DaveM@34 3
DaveM@34 4 row =
DaveM@34 5
DaveM@34 6 8976
DaveM@34 7
DaveM@34 8 Row: 8976, pDepth = 2, loss = 0.002673
DaveM@34 9
DaveM@34 10 Decision tree for classification
DaveM@34 11 1 if first_peak_weight_mean<0.0357145 then node 2 elseif first_peak_weight_mean>=0.0357145 then node 3 else 8975
DaveM@34 12 2 class = 8974
DaveM@34 13 3 class = 8975
DaveM@34 14
DaveM@34 15
DaveM@34 16 row =
DaveM@34 17
DaveM@34 18 8974
DaveM@34 19
DaveM@34 20 Row: 8974, pDepth = 20, loss = 0.079383
DaveM@34 21
DaveM@34 22 Decision tree for classification
DaveM@34 23 1 if silence_rate_60dB_mean<0.495098 then node 2 elseif silence_rate_60dB_mean>=0.495098 then node 3 else 8966
DaveM@34 24 2 class = 8963
DaveM@34 25 3 class = 8966
DaveM@34 26
DaveM@34 27
DaveM@34 28 row =
DaveM@34 29
DaveM@34 30 8975
DaveM@34 31
DaveM@34 32 Row: 8975, pDepth = 37, loss = 0.153664
DaveM@34 33
DaveM@34 34 Decision tree for classification
DaveM@34 35 1 if silence_rate_60dB_mean<0.47305 then node 2 elseif silence_rate_60dB_mean>=0.47305 then node 3 else 8972
DaveM@34 36 2 class = 8973
DaveM@34 37 3 class = 8972
DaveM@34 38
DaveM@34 39
DaveM@34 40 row =
DaveM@34 41
DaveM@34 42 8963
DaveM@34 43
DaveM@34 44 Row: 8963, pDepth = 11, loss = 0.102637
DaveM@34 45
DaveM@34 46 Decision tree for classification
DaveM@34 47 1 if spectral_decrease_mean<0.866593 then node 2 elseif spectral_decrease_mean>=0.866593 then node 3 else 8959
DaveM@34 48 2 class = 8930
DaveM@34 49 3 class = 8959
DaveM@34 50
DaveM@34 51
DaveM@34 52 row =
DaveM@34 53
DaveM@34 54 8966
DaveM@34 55
DaveM@34 56 Row: 8966, pDepth = 16, loss = 0.129073
DaveM@34 57
DaveM@34 58 Decision tree for classification
DaveM@34 59 1 if spectral_centroid_max<0.413299 then node 2 elseif spectral_centroid_max>=0.413299 then node 3 else 8956
DaveM@34 60 2 class = 8928
DaveM@34 61 3 class = 8956
DaveM@34 62
DaveM@34 63
DaveM@34 64 row =
DaveM@34 65
DaveM@34 66 8972
DaveM@34 67
DaveM@34 68 Row: 8972, pDepth = 15, loss = 0.112521
DaveM@34 69
DaveM@34 70 Decision tree for classification
DaveM@34 71 1 if second_peak_bpm_max<0.262195 then node 2 elseif second_peak_bpm_max>=0.262195 then node 3 else 8971
DaveM@34 72 2 class = 8971
DaveM@34 73 3 class = 8969
DaveM@34 74
DaveM@34 75
DaveM@34 76 row =
DaveM@34 77
DaveM@34 78 8973
DaveM@34 79
DaveM@34 80 Row: 8973, pDepth = 16, loss = 0.152279
DaveM@34 81
DaveM@34 82 Decision tree for classification
DaveM@34 83 1 if second_peak_weight_min<0.0616035 then node 2 elseif second_peak_weight_min>=0.0616035 then node 3 else 8970
DaveM@34 84 2 class = 8970
DaveM@34 85 3 class = 8967
DaveM@34 86
DaveM@34 87
DaveM@34 88 row =
DaveM@34 89
DaveM@34 90 8930
DaveM@34 91
DaveM@34 92 Row: 8930, pDepth = 3, loss = 0.135417
DaveM@34 93
DaveM@34 94 Decision tree for classification
DaveM@34 95 1 if scvalleys_min_3<0.509141 then node 2 elseif scvalleys_min_3>=0.509141 then node 3 else 8879
DaveM@34 96 2 class = 8879
DaveM@34 97 3 class = 8863
DaveM@34 98
DaveM@34 99
DaveM@34 100 row =
DaveM@34 101
DaveM@34 102 8959
DaveM@34 103
DaveM@34 104 Row: 8959, pDepth = 11, loss = 0.145977
DaveM@34 105
DaveM@34 106 Decision tree for classification
DaveM@34 107 1 if spectral_flatness_db_mean<0.271369 then node 2 elseif spectral_flatness_db_mean>=0.271369 then node 3 else 8953
DaveM@34 108 2 class = 8953
DaveM@34 109 3 class = 8934
DaveM@34 110
DaveM@34 111
DaveM@34 112 row =
DaveM@34 113
DaveM@34 114 8928
DaveM@34 115
DaveM@34 116 Row: 8928, pDepth = 8, loss = 0.099029
DaveM@34 117
DaveM@34 118 Decision tree for classification
DaveM@34 119 1 if silence_rate_30dB_dmean2<0.017544 then node 2 elseif silence_rate_30dB_dmean2>=0.017544 then node 3 else 8904
DaveM@34 120 2 class = 8904
DaveM@34 121 3 class = 8903
DaveM@34 122
DaveM@34 123
DaveM@34 124 row =
DaveM@34 125
DaveM@34 126 8956
DaveM@34 127
DaveM@34 128 Row: 8956, pDepth = 12, loss = 0.124884
DaveM@34 129
DaveM@34 130 Decision tree for classification
DaveM@34 131 1 if gfcc_median_1<0.520562 then node 2 elseif gfcc_median_1>=0.520562 then node 3 else 8950
DaveM@34 132 2 if beats_loudness_band_ratio_mean_5<0.541448 then node 4 elseif beats_loudness_band_ratio_mean_5>=0.541448 then node 5 else 8923
DaveM@34 133 3 class = 8950
DaveM@34 134 4 class = 8923
DaveM@34 135 5 class = 8950
DaveM@34 136
DaveM@34 137
DaveM@34 138 row =
DaveM@34 139
DaveM@34 140 8969
DaveM@34 141
DaveM@34 142 Row: 8969, pDepth = 18, loss = 0.185809
DaveM@34 143
DaveM@34 144 Decision tree for classification
DaveM@34 145 1 if spectral_energy_var<0.0002945 then node 2 elseif spectral_energy_var>=0.0002945 then node 3 else 8960
DaveM@34 146 2 if second_peak_bpm_min<0.593496 then node 4 elseif second_peak_bpm_min>=0.593496 then node 5 else 8949
DaveM@34 147 3 if second_peak_bpm_min<0.310976 then node 6 elseif second_peak_bpm_min>=0.310976 then node 7 else 8960
DaveM@34 148 4 class = 8949
DaveM@34 149 5 if spectral_energy_var<1.5e-06 then node 8 elseif spectral_energy_var>=1.5e-06 then node 9 else 8949
DaveM@34 150 6 class = 8949
DaveM@34 151 7 class = 8960
DaveM@34 152 8 class = 8949
DaveM@34 153 9 if spectral_decrease_mean<0.900607 then node 10 elseif spectral_decrease_mean>=0.900607 then node 11 else 8960
DaveM@34 154 10 if strongdecay<0.0611825 then node 12 elseif strongdecay>=0.0611825 then node 13 else 8960
DaveM@34 155 11 class = 8949
DaveM@34 156 12 class = 8960
DaveM@34 157 13 if spectral_decrease_var<3.5e-05 then node 14 elseif spectral_decrease_var>=3.5e-05 then node 15 else 8949
DaveM@34 158 14 if spectral_decrease_mean<0.900432 then node 16 elseif spectral_decrease_mean>=0.900432 then node 17 else 8949
DaveM@34 159 15 class = 8960
DaveM@34 160 16 class = 8949
DaveM@34 161 17 class = 8960
DaveM@34 162
DaveM@34 163
DaveM@34 164 row =
DaveM@34 165
DaveM@34 166 8971
DaveM@34 167
DaveM@34 168 Row: 8971, pDepth = 23, loss = 0.178851
DaveM@34 169
DaveM@34 170 Decision tree for classification
DaveM@34 171 1 if first_peak_weight_max<0.894445 then node 2 elseif first_peak_weight_max>=0.894445 then node 3 else 8965
DaveM@34 172 2 if beats_loudness_band_ratio_max_5<0.671258 then node 4 elseif beats_loudness_band_ratio_max_5>=0.671258 then node 5 else 8962
DaveM@34 173 3 class = 8965
DaveM@34 174 4 class = 8962
DaveM@34 175 5 class = 8965
DaveM@34 176
DaveM@34 177
DaveM@34 178 row =
DaveM@34 179
DaveM@34 180 8967
DaveM@34 181
DaveM@34 182 Row: 8967, pDepth = 4, loss = 0.037081
DaveM@34 183
DaveM@34 184 Decision tree for classification
DaveM@34 185 1 if spectral_spread_mean<0.015563 then node 2 elseif spectral_spread_mean>=0.015563 then node 3 else 8961
DaveM@34 186 2 class = 8964
DaveM@34 187 3 class = 8961
DaveM@34 188
DaveM@34 189
DaveM@34 190 row =
DaveM@34 191
DaveM@34 192 8970
DaveM@34 193
DaveM@34 194 Row: 8970, pDepth = 1, loss = 1.000000
DaveM@34 195
DaveM@34 196 Decision tree for classification
DaveM@34 197 1 if spectral_spread_mean<0.015563 then node 2 elseif spectral_spread_mean>=0.015563 then node 3 else 8961
DaveM@34 198 2 class = 8964
DaveM@34 199 3 class = 8961
DaveM@34 200
DaveM@34 201
DaveM@34 202 row =
DaveM@34 203
DaveM@34 204 8863
DaveM@34 205
DaveM@34 206 Row: 8863, pDepth = 3, loss = 0.087912
DaveM@34 207
DaveM@34 208 Decision tree for classification
DaveM@34 209 1 if mfcc_dmean_5<0.243998 then node 2 elseif mfcc_dmean_5>=0.243998 then node 3 else 8745
DaveM@34 210 2 class = 8745
DaveM@34 211 3 class = 8722
DaveM@34 212
DaveM@34 213
DaveM@34 214 row =
DaveM@34 215
DaveM@34 216 8879
DaveM@34 217
DaveM@34 218 Row: 8879, pDepth = 1, loss = 0.089109
DaveM@34 219
DaveM@34 220 Decision tree for classification
DaveM@34 221 1 if spectral_energyband_middle_high_mean<0.0070985 then node 2 elseif spectral_energyband_middle_high_mean>=0.0070985 then node 3 else 8841
DaveM@34 222 2 class = 8693
DaveM@34 223 3 class = 8841
DaveM@34 224
DaveM@34 225
DaveM@34 226 row =
DaveM@34 227
DaveM@34 228 8934
DaveM@34 229
DaveM@34 230 Row: 8934, pDepth = 1, loss = 1.000000
DaveM@34 231
DaveM@34 232 Decision tree for classification
DaveM@34 233 1 if spectral_energyband_middle_high_mean<0.0070985 then node 2 elseif spectral_energyband_middle_high_mean>=0.0070985 then node 3 else 8841
DaveM@34 234 2 class = 8693
DaveM@34 235 3 class = 8841
DaveM@34 236
DaveM@34 237
DaveM@34 238 row =
DaveM@34 239
DaveM@34 240 8953
DaveM@34 241
DaveM@34 242 Row: 8953, pDepth = 12, loss = 0.169591
DaveM@34 243
DaveM@34 244 Decision tree for classification
DaveM@34 245 1 if pitch_mean<0.103532 then node 2 elseif pitch_mean>=0.103532 then node 3 else 8939
DaveM@34 246 2 class = 8939
DaveM@34 247 3 class = 8943
DaveM@34 248
DaveM@34 249
DaveM@34 250 row =
DaveM@34 251
DaveM@34 252 8903
DaveM@34 253
DaveM@34 254 Row: 8903, pDepth = 2, loss = 0.065089
DaveM@34 255
DaveM@34 256 Decision tree for classification
DaveM@34 257 1 if scvalleys_mean_0<0.660545 then node 2 elseif scvalleys_mean_0>=0.660545 then node 3 else 8812
DaveM@34 258 2 class = 8735
DaveM@34 259 3 class = 8812
DaveM@34 260
DaveM@34 261
DaveM@34 262 row =
DaveM@34 263
DaveM@34 264 8904
DaveM@34 265
DaveM@34 266 Row: 8904, pDepth = 5, loss = 0.127168
DaveM@34 267
DaveM@34 268 Decision tree for classification
DaveM@34 269 1 if beats_loudness_band_ratio_max_4<5e-07 then node 2 elseif beats_loudness_band_ratio_max_4>=5e-07 then node 3 else 8854
DaveM@34 270 2 class = 8815
DaveM@34 271 3 class = 8854
DaveM@34 272
DaveM@34 273
DaveM@34 274 row =
DaveM@34 275
DaveM@34 276 8923
DaveM@34 277
DaveM@34 278 Row: 8923, pDepth = 7, loss = 0.188679
DaveM@34 279
DaveM@34 280 Decision tree for classification
DaveM@34 281 1 if gfcc_dmean_1<0.15129 then node 2 elseif gfcc_dmean_1>=0.15129 then node 3 else 8898
DaveM@34 282 2 if beats_loudness_band_ratio_max_0<0.73234 then node 4 elseif beats_loudness_band_ratio_max_0>=0.73234 then node 5 else 8901
DaveM@34 283 3 class = 8898
DaveM@34 284 4 class = 8901
DaveM@34 285 5 class = 8898
DaveM@34 286
DaveM@34 287
DaveM@34 288 row =
DaveM@34 289
DaveM@34 290 8950
DaveM@34 291
DaveM@34 292 Row: 8950, pDepth = 6, loss = 0.056291
DaveM@34 293
DaveM@34 294 Decision tree for classification
DaveM@34 295 1 if beats_loudness_band_ratio_mean_5<0.258765 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.258765 then node 3 else 8922
DaveM@34 296 2 class = 8922
DaveM@34 297 3 class = 8909
DaveM@34 298
DaveM@34 299
DaveM@34 300 row =
DaveM@34 301
DaveM@34 302 8949
DaveM@34 303
DaveM@34 304 Row: 8949, pDepth = 11, loss = 0.166455
DaveM@34 305
DaveM@34 306 Decision tree for classification
DaveM@34 307 1 if beats_loudness_band_ratio_mean_5<0.414874 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.414874 then node 3 else 8948
DaveM@34 308 2 class = 8948
DaveM@34 309 3 class = 8926
DaveM@34 310
DaveM@34 311
DaveM@34 312 row =
DaveM@34 313
DaveM@34 314 8960
DaveM@34 315
DaveM@34 316 Row: 8960, pDepth = 17, loss = 0.197952
DaveM@34 317
DaveM@34 318 Decision tree for classification
DaveM@34 319 1 if max_der_before_max_min<0.397496 then node 2 elseif max_der_before_max_min>=0.397496 then node 3 else 8947
DaveM@34 320 2 class = 8944
DaveM@34 321 3 class = 8947
DaveM@34 322
DaveM@34 323
DaveM@34 324 row =
DaveM@34 325
DaveM@34 326 8962
DaveM@34 327
DaveM@34 328 Row: 8962, pDepth = 11, loss = 0.144491
DaveM@34 329
DaveM@34 330 Decision tree for classification
DaveM@34 331 1 if spectral_skewness_median<0.0668925 then node 2 elseif spectral_skewness_median>=0.0668925 then node 3 else 8958
DaveM@34 332 2 class = 8958
DaveM@34 333 3 class = 8931
DaveM@34 334
DaveM@34 335
DaveM@34 336 row =
DaveM@34 337
DaveM@34 338 8965
DaveM@34 339
DaveM@34 340 Row: 8965, pDepth = 18, loss = 0.195359
DaveM@34 341
DaveM@34 342 Decision tree for classification
DaveM@34 343 1 if scvalleys_mean_0<0.684385 then node 2 elseif scvalleys_mean_0>=0.684385 then node 3 else 8957
DaveM@34 344 2 if scvalleys_min_2<0.395652 then node 4 elseif scvalleys_min_2>=0.395652 then node 5 else 8937
DaveM@34 345 3 class = 8957
DaveM@34 346 4 class = 8937
DaveM@34 347 5 class = 8957
DaveM@34 348
DaveM@34 349
DaveM@34 350 row =
DaveM@34 351
DaveM@34 352 8961
DaveM@34 353
DaveM@34 354 Row: 8961, pDepth = 3, loss = 0.022222
DaveM@34 355
DaveM@34 356 Decision tree for classification
DaveM@34 357 1 if strongdecay<0.077154 then node 2 elseif strongdecay>=0.077154 then node 3 else 8952
DaveM@34 358 2 class = 8885
DaveM@34 359 3 class = 8952
DaveM@34 360
DaveM@34 361
DaveM@34 362 row =
DaveM@34 363
DaveM@34 364 8964
DaveM@34 365
DaveM@34 366 Row: 8964, pDepth = 1, loss = 1.000000
DaveM@34 367
DaveM@34 368 Decision tree for classification
DaveM@34 369 1 if strongdecay<0.077154 then node 2 elseif strongdecay>=0.077154 then node 3 else 8952
DaveM@34 370 2 class = 8885
DaveM@34 371 3 class = 8952
DaveM@34 372
DaveM@34 373
DaveM@34 374 row =
DaveM@34 375
DaveM@34 376 8942
DaveM@34 377
DaveM@34 378 Row: 8942, pDepth = 0, loss = 1.000000
DaveM@34 379
DaveM@34 380 Decision tree for classification
DaveM@34 381 1 if strongdecay<0.077154 then node 2 elseif strongdecay>=0.077154 then node 3 else 8952
DaveM@34 382 2 class = 8885
DaveM@34 383 3 class = 8952
DaveM@34 384
DaveM@34 385
DaveM@34 386 row =
DaveM@34 387
DaveM@34 388 8968
DaveM@34 389
DaveM@34 390 Row: 8968, pDepth = 14, loss = 0.165846
DaveM@34 391
DaveM@34 392 Decision tree for classification
DaveM@34 393 1 if gfcc_min_2<0.387343 then node 2 elseif gfcc_min_2>=0.387343 then node 3 else 8954
DaveM@34 394 2 class = 8951
DaveM@34 395 3 class = 8954
DaveM@34 396
DaveM@34 397
DaveM@34 398 row =
DaveM@34 399
DaveM@34 400 8722
DaveM@34 401
DaveM@34 402 Row: 8722, pDepth = 1, loss = 1.000000
DaveM@34 403
DaveM@34 404 Decision tree for classification
DaveM@34 405 1 if gfcc_min_2<0.387343 then node 2 elseif gfcc_min_2>=0.387343 then node 3 else 8954
DaveM@34 406 2 class = 8951
DaveM@34 407 3 class = 8954
DaveM@34 408
DaveM@34 409
DaveM@34 410 row =
DaveM@34 411
DaveM@34 412 8745
DaveM@34 413
DaveM@34 414 Row: 8745, pDepth = 1, loss = 0.057971
DaveM@34 415
DaveM@34 416 Decision tree for classification
DaveM@34 417 1 if spectral_energyband_high_max<0.193083 then node 2 elseif spectral_energyband_high_max>=0.193083 then node 3 else 8670
DaveM@34 418 2 class = 8670
DaveM@34 419 3 class = 8018
DaveM@34 420
DaveM@34 421
DaveM@34 422 row =
DaveM@34 423
DaveM@34 424 8693
DaveM@34 425
DaveM@34 426 Row: 8693, pDepth = 1, loss = 1.000000
DaveM@34 427
DaveM@34 428 Decision tree for classification
DaveM@34 429 1 if spectral_energyband_high_max<0.193083 then node 2 elseif spectral_energyband_high_max>=0.193083 then node 3 else 8670
DaveM@34 430 2 class = 8670
DaveM@34 431 3 class = 8018
DaveM@34 432
DaveM@34 433
DaveM@34 434 row =
DaveM@34 435
DaveM@34 436 8841
DaveM@34 437
DaveM@34 438 Row: 8841, pDepth = 2, loss = 0.181818
DaveM@34 439
DaveM@34 440 Decision tree for classification
DaveM@34 441 1 if zerocrossingrate_dmean2<0.091876 then node 2 elseif zerocrossingrate_dmean2>=0.091876 then node 3 else 8771
DaveM@34 442 2 class = 8771
DaveM@34 443 3 if erb_bands_max_4<0.009823 then node 4 elseif erb_bands_max_4>=0.009823 then node 5 else 8771
DaveM@34 444 4 class = 8794
DaveM@34 445 5 class = 8771
DaveM@34 446
DaveM@34 447
DaveM@34 448 row =
DaveM@34 449
DaveM@34 450 7760
DaveM@34 451
DaveM@34 452 Row: 7760, pDepth = 1, loss = 1.000000
DaveM@34 453
DaveM@34 454 Decision tree for classification
DaveM@34 455 1 if zerocrossingrate_dmean2<0.091876 then node 2 elseif zerocrossingrate_dmean2>=0.091876 then node 3 else 8771
DaveM@34 456 2 class = 8771
DaveM@34 457 3 if erb_bands_max_4<0.009823 then node 4 elseif erb_bands_max_4>=0.009823 then node 5 else 8771
DaveM@34 458 4 class = 8794
DaveM@34 459 5 class = 8771
DaveM@34 460
DaveM@34 461
DaveM@34 462 row =
DaveM@34 463
DaveM@34 464 8913
DaveM@34 465
DaveM@34 466 Row: 8913, pDepth = 3, loss = 0.068966
DaveM@34 467
DaveM@34 468 Decision tree for classification
DaveM@34 469 1 if spectral_energyband_middle_low_median<0.0083265 then node 2 elseif spectral_energyband_middle_low_median>=0.0083265 then node 3 else 8875
DaveM@34 470 2 class = 8875
DaveM@34 471 3 class = 8774
DaveM@34 472
DaveM@34 473
DaveM@34 474 row =
DaveM@34 475
DaveM@34 476 8939
DaveM@34 477
DaveM@34 478 Row: 8939, pDepth = 5, loss = 0.118834
DaveM@34 479
DaveM@34 480 Decision tree for classification
DaveM@34 481 1 if scvalleys_min_4<0.0737265 then node 2 elseif scvalleys_min_4>=0.0737265 then node 3 else 8925
DaveM@34 482 2 class = 8804
DaveM@34 483 3 class = 8925
DaveM@34 484
DaveM@34 485
DaveM@34 486 row =
DaveM@34 487
DaveM@34 488 8943
DaveM@34 489
DaveM@34 490 Row: 8943, pDepth = 3, loss = 0.105042
DaveM@34 491
DaveM@34 492 Decision tree for classification
DaveM@34 493 1 if spectral_contrast_dvar_5<0.231631 then node 2 elseif spectral_contrast_dvar_5>=0.231631 then node 3 else 8906
DaveM@34 494 2 class = 8906
DaveM@34 495 3 class = 8911
DaveM@34 496
DaveM@34 497
DaveM@34 498 row =
DaveM@34 499
DaveM@34 500 8735
DaveM@34 501
DaveM@34 502 Row: 8735, pDepth = 1, loss = 0.051282
DaveM@34 503
DaveM@34 504 Decision tree for classification
DaveM@34 505 1 if inharmonicity_mean<0.0605425 then node 2 elseif inharmonicity_mean>=0.0605425 then node 3 else 8380
DaveM@34 506 2 class = 8171
DaveM@34 507 3 class = 8380
DaveM@34 508
DaveM@34 509
DaveM@34 510 row =
DaveM@34 511
DaveM@34 512 8812
DaveM@34 513
DaveM@34 514 Row: 8812, pDepth = 2, loss = 0.092308
DaveM@34 515
DaveM@34 516 Decision tree for classification
DaveM@34 517 1 if beats_loudness_band_ratio_max_5<0.0128005 then node 2 elseif beats_loudness_band_ratio_max_5>=0.0128005 then node 3 else 8759
DaveM@34 518 2 class = 8494
DaveM@34 519 3 class = 8759
DaveM@34 520
DaveM@34 521
DaveM@34 522 row =
DaveM@34 523
DaveM@34 524 8815
DaveM@34 525
DaveM@34 526 Row: 8815, pDepth = 3, loss = 0.137255
DaveM@34 527
DaveM@34 528 Decision tree for classification
DaveM@34 529 1 if scvalleys_mean_2<0.715351 then node 2 elseif scvalleys_mean_2>=0.715351 then node 3 else 8697
DaveM@34 530 2 class = 8697
DaveM@34 531 3 class = 8302
DaveM@34 532
DaveM@34 533
DaveM@34 534 row =
DaveM@34 535
DaveM@34 536 8854
DaveM@34 537
DaveM@34 538 Row: 8854, pDepth = 2, loss = 0.186528
DaveM@34 539
DaveM@34 540 Decision tree for classification
DaveM@34 541 1 if scvalleys_dvar_3<0.0380355 then node 2 elseif scvalleys_dvar_3>=0.0380355 then node 3 else 8765
DaveM@34 542 2 if pitch_dmean2<0.0069445 then node 4 elseif pitch_dmean2>=0.0069445 then node 5 else 8765
DaveM@34 543 3 if barkbands_dvar2_16<5e-07 then node 6 elseif barkbands_dvar2_16>=5e-07 then node 7 else 8734
DaveM@34 544 4 class = 8734
DaveM@34 545 5 if pitch_dmean2<0.10212 then node 8 elseif pitch_dmean2>=0.10212 then node 9 else 8765
DaveM@34 546 6 if pitch_dmean2<0.133033 then node 10 elseif pitch_dmean2>=0.133033 then node 11 else 8765
DaveM@34 547 7 if erb_bands_var_14<5e-07 then node 12 elseif erb_bands_var_14>=5e-07 then node 13 else 8734
DaveM@34 548 8 class = 8765
DaveM@34 549 9 if scvalleys_dvar_3<0.021109 then node 14 elseif scvalleys_dvar_3>=0.021109 then node 15 else 8734
DaveM@34 550 10 if pitch_dmean2<0.0082305 then node 16 elseif pitch_dmean2>=0.0082305 then node 17 else 8765
DaveM@34 551 11 class = 8734
DaveM@34 552 12 class = 8734
DaveM@34 553 13 if erb_bands_var_14<1.4e-05 then node 18 elseif erb_bands_var_14>=1.4e-05 then node 19 else 8734
DaveM@34 554 14 class = 8734
DaveM@34 555 15 class = 8765
DaveM@34 556 16 class = 8734
DaveM@34 557 17 class = 8765
DaveM@34 558 18 if scvalleys_dvar_3<0.0476555 then node 20 elseif scvalleys_dvar_3>=0.0476555 then node 21 else 8765
DaveM@34 559 19 class = 8734
DaveM@34 560 20 class = 8765
DaveM@34 561 21 class = 8734
DaveM@34 562
DaveM@34 563
DaveM@34 564 row =
DaveM@34 565
DaveM@34 566 8898
DaveM@34 567
DaveM@34 568 Row: 8898, pDepth = 4, loss = 0.075099
DaveM@34 569
DaveM@34 570 Decision tree for classification
DaveM@34 571 1 if barkbands_dmean_18<0.0001645 then node 2 elseif barkbands_dmean_18>=0.0001645 then node 3 else 8886
DaveM@34 572 2 if barkbands_dmean_15<1.15e-05 then node 4 elseif barkbands_dmean_15>=1.15e-05 then node 5 else 8362
DaveM@34 573 3 class = 8886
DaveM@34 574 4 class = 8886
DaveM@34 575 5 class = 8362
DaveM@34 576
DaveM@34 577
DaveM@34 578 row =
DaveM@34 579
DaveM@34 580 8901
DaveM@34 581
DaveM@34 582 Row: 8901, pDepth = 4, loss = 0.058036
DaveM@34 583
DaveM@34 584 Decision tree for classification
DaveM@34 585 1 if tristimulus_median_0<0.0025435 then node 2 elseif tristimulus_median_0>=0.0025435 then node 3 else 8860
DaveM@34 586 2 class = 8831
DaveM@34 587 3 class = 8860
DaveM@34 588
DaveM@34 589
DaveM@34 590 row =
DaveM@34 591
DaveM@34 592 8909
DaveM@34 593
DaveM@34 594 Row: 8909, pDepth = 5, loss = 0.141509
DaveM@34 595
DaveM@34 596 Decision tree for classification
DaveM@34 597 1 if zerocrossingrate_var<0.0237885 then node 2 elseif zerocrossingrate_var>=0.0237885 then node 3 else 8783
DaveM@34 598 2 class = 8741
DaveM@34 599 3 class = 8783
DaveM@34 600
DaveM@34 601
DaveM@34 602 row =
DaveM@34 603
DaveM@34 604 8922
DaveM@34 605
DaveM@34 606 Row: 8922, pDepth = 6, loss = 0.145408
DaveM@34 607
DaveM@34 608 Decision tree for classification
DaveM@34 609 1 if silence_rate_30dB_mean<0.974647 then node 2 elseif silence_rate_30dB_mean>=0.974647 then node 3 else 8880
DaveM@34 610 2 class = 8858
DaveM@34 611 3 class = 8880
DaveM@34 612
DaveM@34 613
DaveM@34 614 row =
DaveM@34 615
DaveM@34 616 8926
DaveM@34 617
DaveM@34 618 Row: 8926, pDepth = 4, loss = 0.105263
DaveM@34 619
DaveM@34 620 Decision tree for classification
DaveM@34 621 1 if spectral_spread_dvar2<0.152478 then node 2 elseif spectral_spread_dvar2>=0.152478 then node 3 else 8897
DaveM@34 622 2 class = 8897
DaveM@34 623 3 class = 8881
DaveM@34 624
DaveM@34 625
DaveM@34 626 row =
DaveM@34 627
DaveM@34 628 8948
DaveM@34 629
DaveM@34 630 Row: 8948, pDepth = 5, loss = 0.164360
DaveM@34 631
DaveM@34 632 Decision tree for classification
DaveM@34 633 1 if first_peak_spread_max<0.099624 then node 2 elseif first_peak_spread_max>=0.099624 then node 3 else 8933
DaveM@34 634 2 class = 8895
DaveM@34 635 3 class = 8933
DaveM@34 636
DaveM@34 637
DaveM@34 638 row =
DaveM@34 639
DaveM@34 640 8944
DaveM@34 641
DaveM@34 642 Row: 8944, pDepth = 10, loss = 0.182203
DaveM@34 643
DaveM@34 644 Decision tree for classification
DaveM@34 645 1 if spectral_decrease_min<0.975873 then node 2 elseif spectral_decrease_min>=0.975873 then node 3 else 8905
DaveM@34 646 2 class = 8905
DaveM@34 647 3 class = 8888
DaveM@34 648
DaveM@34 649
DaveM@34 650 row =
DaveM@34 651
DaveM@34 652 8947
DaveM@34 653
DaveM@34 654 Row: 8947, pDepth = 9, loss = 0.137143
DaveM@34 655
DaveM@34 656 Decision tree for classification
DaveM@34 657 1 if scvalleys_min_5<0.329405 then node 2 elseif scvalleys_min_5>=0.329405 then node 3 else 8940
DaveM@34 658 2 class = 8940
DaveM@34 659 3 class = 8893
DaveM@34 660
DaveM@34 661
DaveM@34 662 row =
DaveM@34 663
DaveM@34 664 8931
DaveM@34 665
DaveM@34 666 Row: 8931, pDepth = 1, loss = 1.000000
DaveM@34 667
DaveM@34 668 Decision tree for classification
DaveM@34 669 1 if scvalleys_min_5<0.329405 then node 2 elseif scvalleys_min_5>=0.329405 then node 3 else 8940
DaveM@34 670 2 class = 8940
DaveM@34 671 3 class = 8893
DaveM@34 672
DaveM@34 673
DaveM@34 674 row =
DaveM@34 675
DaveM@34 676 8958
DaveM@34 677
DaveM@34 678 Row: 8958, pDepth = 8, loss = 0.084151
DaveM@34 679
DaveM@34 680 Decision tree for classification
DaveM@34 681 1 if scvalleys_max_1<0.574793 then node 2 elseif scvalleys_max_1>=0.574793 then node 3 else 8955
DaveM@34 682 2 class = 8935
DaveM@34 683 3 class = 8955
DaveM@34 684
DaveM@34 685
DaveM@34 686 row =
DaveM@34 687
DaveM@34 688 8937
DaveM@34 689
DaveM@34 690 Row: 8937, pDepth = 7, loss = 0.156306
DaveM@34 691
DaveM@34 692 Decision tree for classification
DaveM@34 693 1 if beats_loudness_band_ratio_mean_5<0.146952 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.146952 then node 3 else 8915
DaveM@34 694 2 if pitch_salience_mean<0.568618 then node 4 elseif pitch_salience_mean>=0.568618 then node 5 else 8915
DaveM@34 695 3 class = 8920
DaveM@34 696 4 class = 8920
DaveM@34 697 5 class = 8915
DaveM@34 698
DaveM@34 699
DaveM@34 700 row =
DaveM@34 701
DaveM@34 702 8957
DaveM@34 703
DaveM@34 704 Row: 8957, pDepth = 9, loss = 0.097025
DaveM@34 705
DaveM@34 706 Decision tree for classification
DaveM@34 707 1 if beats_loudness_band_ratio_mean_5<0.280122 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.280122 then node 3 else 8936
DaveM@34 708 2 class = 8936
DaveM@34 709 3 class = 8919
DaveM@34 710
DaveM@34 711
DaveM@34 712 row =
DaveM@34 713
DaveM@34 714 8885
DaveM@34 715
DaveM@34 716 Row: 8885, pDepth = 1, loss = 0.039370
DaveM@34 717
DaveM@34 718 Decision tree for classification
DaveM@34 719 1 if beats_loudness_band_ratio_mean_0<0.230278 then node 2 elseif beats_loudness_band_ratio_mean_0>=0.230278 then node 3 else 8760
DaveM@34 720 2 class = 8707
DaveM@34 721 3 class = 8760
DaveM@34 722
DaveM@34 723
DaveM@34 724 row =
DaveM@34 725
DaveM@34 726 8952
DaveM@34 727
DaveM@34 728 Row: 8952, pDepth = 10, loss = 0.158516
DaveM@34 729
DaveM@34 730 Decision tree for classification
DaveM@34 731 1 if first_peak_spread_min<0.010566 then node 2 elseif first_peak_spread_min>=0.010566 then node 3 else 8918
DaveM@34 732 2 class = 8932
DaveM@34 733 3 if scvalleys_min_3<0.337108 then node 4 elseif scvalleys_min_3>=0.337108 then node 5 else 8918
DaveM@34 734 4 class = 8918
DaveM@34 735 5 if second_peak_spread_max<0.459388 then node 6 elseif second_peak_spread_max>=0.459388 then node 7 else 8918
DaveM@34 736 6 class = 8932
DaveM@34 737 7 class = 8918
DaveM@34 738
DaveM@34 739
DaveM@34 740 row =
DaveM@34 741
DaveM@34 742 8865
DaveM@34 743
DaveM@34 744 Row: 8865, pDepth = 2, loss = 0.060241
DaveM@34 745
DaveM@34 746 Decision tree for classification
DaveM@34 747 1 if spectral_entropy_mean<0.228026 then node 2 elseif spectral_entropy_mean>=0.228026 then node 3 else 8677
DaveM@34 748 2 class = 8540
DaveM@34 749 3 class = 8677
DaveM@34 750
DaveM@34 751
DaveM@34 752 row =
DaveM@34 753
DaveM@34 754 8945
DaveM@34 755
DaveM@34 756 Row: 8945, pDepth = 1, loss = 1.000000
DaveM@34 757
DaveM@34 758 Decision tree for classification
DaveM@34 759 1 if spectral_entropy_mean<0.228026 then node 2 elseif spectral_entropy_mean>=0.228026 then node 3 else 8677
DaveM@34 760 2 class = 8540
DaveM@34 761 3 class = 8677
DaveM@34 762
DaveM@34 763
DaveM@34 764 row =
DaveM@34 765
DaveM@34 766 8657
DaveM@34 767
DaveM@34 768 Row: 8657, pDepth = 0, loss = 1.000000
DaveM@34 769
DaveM@34 770 Decision tree for classification
DaveM@34 771 1 if spectral_entropy_mean<0.228026 then node 2 elseif spectral_entropy_mean>=0.228026 then node 3 else 8677
DaveM@34 772 2 class = 8540
DaveM@34 773 3 class = 8677
DaveM@34 774
DaveM@34 775
DaveM@34 776 row =
DaveM@34 777
DaveM@34 778 8723
DaveM@34 779
DaveM@34 780 Row: 8723, pDepth = 1, loss = 1.000000
DaveM@34 781
DaveM@34 782 Decision tree for classification
DaveM@34 783 1 if spectral_entropy_mean<0.228026 then node 2 elseif spectral_entropy_mean>=0.228026 then node 3 else 8677
DaveM@34 784 2 class = 8540
DaveM@34 785 3 class = 8677
DaveM@34 786
DaveM@34 787
DaveM@34 788 row =
DaveM@34 789
DaveM@34 790 8951
DaveM@34 791
DaveM@34 792 Row: 8951, pDepth = 5, loss = 0.193900
DaveM@34 793
DaveM@34 794 Decision tree for classification
DaveM@34 795 1 if first_peak_weight_max<0.775 then node 2 elseif first_peak_weight_max>=0.775 then node 3 else 8941
DaveM@34 796 2 class = 8941
DaveM@34 797 3 class = 8938
DaveM@34 798
DaveM@34 799
DaveM@34 800 row =
DaveM@34 801
DaveM@34 802 8954
DaveM@34 803
DaveM@34 804 Row: 8954, pDepth = 12, loss = 0.196311
DaveM@34 805
DaveM@34 806 Decision tree for classification
DaveM@34 807 1 if zerocrossingrate_mean<0.10335 then node 2 elseif zerocrossingrate_mean>=0.10335 then node 3 else 8929
DaveM@34 808 2 class = 8924
DaveM@34 809 3 class = 8929
DaveM@34 810
DaveM@34 811
DaveM@34 812 row =
DaveM@34 813
DaveM@34 814 8579
DaveM@34 815
DaveM@34 816 Row: 8579, pDepth = 0, loss = 1.000000
DaveM@34 817
DaveM@34 818 Decision tree for classification
DaveM@34 819 1 if zerocrossingrate_mean<0.10335 then node 2 elseif zerocrossingrate_mean>=0.10335 then node 3 else 8929
DaveM@34 820 2 class = 8924
DaveM@34 821 3 class = 8929
DaveM@34 822
DaveM@34 823
DaveM@34 824 row =
DaveM@34 825
DaveM@34 826 8628
DaveM@34 827
DaveM@34 828 Row: 8628, pDepth = 1, loss = 1.000000
DaveM@34 829
DaveM@34 830 Decision tree for classification
DaveM@34 831 1 if zerocrossingrate_mean<0.10335 then node 2 elseif zerocrossingrate_mean>=0.10335 then node 3 else 8929
DaveM@34 832 2 class = 8924
DaveM@34 833 3 class = 8929
DaveM@34 834
DaveM@34 835
DaveM@34 836 row =
DaveM@34 837
DaveM@34 838 8018
DaveM@34 839
DaveM@34 840 Row: 8018, pDepth = 1, loss = 1.000000
DaveM@34 841
DaveM@34 842 Decision tree for classification
DaveM@34 843 1 if zerocrossingrate_mean<0.10335 then node 2 elseif zerocrossingrate_mean>=0.10335 then node 3 else 8929
DaveM@34 844 2 class = 8924
DaveM@34 845 3 class = 8929
DaveM@34 846
DaveM@34 847
DaveM@34 848 row =
DaveM@34 849
DaveM@34 850 8670
DaveM@34 851
DaveM@34 852 Row: 8670, pDepth = 1, loss = 0.118644
DaveM@34 853
DaveM@34 854 Decision tree for classification
DaveM@34 855 1 if gfcc_median_2<0.442238 then node 2 elseif gfcc_median_2>=0.442238 then node 3 else 8606
DaveM@34 856 2 class = 8606
DaveM@34 857 3 class = 8650
DaveM@34 858
DaveM@34 859
DaveM@34 860 row =
DaveM@34 861
DaveM@34 862 8184
DaveM@34 863
DaveM@34 864 Row: 8184, pDepth = 0, loss = 1.000000
DaveM@34 865
DaveM@34 866 Decision tree for classification
DaveM@34 867 1 if gfcc_median_2<0.442238 then node 2 elseif gfcc_median_2>=0.442238 then node 3 else 8606
DaveM@34 868 2 class = 8606
DaveM@34 869 3 class = 8650
DaveM@34 870
DaveM@34 871
DaveM@34 872 row =
DaveM@34 873
DaveM@34 874 8637
DaveM@34 875
DaveM@34 876 Row: 8637, pDepth = 1, loss = 1.000000
DaveM@34 877
DaveM@34 878 Decision tree for classification
DaveM@34 879 1 if gfcc_median_2<0.442238 then node 2 elseif gfcc_median_2>=0.442238 then node 3 else 8606
DaveM@34 880 2 class = 8606
DaveM@34 881 3 class = 8650
DaveM@34 882
DaveM@34 883
DaveM@34 884 row =
DaveM@34 885
DaveM@34 886 8771
DaveM@34 887
DaveM@34 888 Row: 8771, pDepth = 1, loss = 0.035714
DaveM@34 889
DaveM@34 890 Decision tree for classification
DaveM@34 891 1 if erb_bands_dmean_3<0.576388 then node 2 elseif erb_bands_dmean_3>=0.576388 then node 3 else 8633
DaveM@34 892 2 class = 8633
DaveM@34 893 3 class = 8430
DaveM@34 894
DaveM@34 895
DaveM@34 896 row =
DaveM@34 897
DaveM@34 898 8794
DaveM@34 899
DaveM@34 900 Row: 8794, pDepth = 1, loss = 0.095238
DaveM@34 901
DaveM@34 902 Decision tree for classification
DaveM@34 903 1 if barkbands_median_2<1.65e-05 then node 2 elseif barkbands_median_2>=1.65e-05 then node 3 else 8217
DaveM@34 904 2 class = 8534
DaveM@34 905 3 class = 8217
DaveM@34 906
DaveM@34 907
DaveM@34 908 row =
DaveM@34 909
DaveM@34 910 5827
DaveM@34 911
DaveM@34 912 Row: 5827, pDepth = 0, loss = 1.000000
DaveM@34 913
DaveM@34 914 Decision tree for classification
DaveM@34 915 1 if barkbands_median_2<1.65e-05 then node 2 elseif barkbands_median_2>=1.65e-05 then node 3 else 8217
DaveM@34 916 2 class = 8534
DaveM@34 917 3 class = 8217
DaveM@34 918
DaveM@34 919
DaveM@34 920 row =
DaveM@34 921
DaveM@34 922 7156
DaveM@34 923
DaveM@34 924 Row: 7156, pDepth = 0, loss = 1.000000
DaveM@34 925
DaveM@34 926 Decision tree for classification
DaveM@34 927 1 if barkbands_median_2<1.65e-05 then node 2 elseif barkbands_median_2>=1.65e-05 then node 3 else 8217
DaveM@34 928 2 class = 8534
DaveM@34 929 3 class = 8217
DaveM@34 930
DaveM@34 931
DaveM@34 932 row =
DaveM@34 933
DaveM@34 934 8774
DaveM@34 935
DaveM@34 936 Row: 8774, pDepth = 2, loss = 0.061224
DaveM@34 937
DaveM@34 938 Decision tree for classification
DaveM@34 939 1 if barkbands_mean_17<8.1e-05 then node 2 elseif barkbands_mean_17>=8.1e-05 then node 3 else 8621
DaveM@34 940 2 class = 8621
DaveM@34 941 3 class = 8312
DaveM@34 942
DaveM@34 943
DaveM@34 944 row =
DaveM@34 945
DaveM@34 946 8875
DaveM@34 947
DaveM@34 948 Row: 8875, pDepth = 3, loss = 0.104000
DaveM@34 949
DaveM@34 950 Decision tree for classification
DaveM@34 951 1 if spectral_flux_max<0.200327 then node 2 elseif spectral_flux_max>=0.200327 then node 3 else 8870
DaveM@34 952 2 class = 8870
DaveM@34 953 3 if pitch_instantaneous_confidence_var<0.108638 then node 4 elseif pitch_instantaneous_confidence_var>=0.108638 then node 5 else 8799
DaveM@34 954 4 class = 8870
DaveM@34 955 5 class = 8799
DaveM@34 956
DaveM@34 957
DaveM@34 958 row =
DaveM@34 959
DaveM@34 960 8804
DaveM@34 961
DaveM@34 962 Row: 8804, pDepth = 2, loss = 0.138614
DaveM@34 963
DaveM@34 964 Decision tree for classification
DaveM@34 965 1 if beats_loudness_band_ratio_mean_5<0.50131 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.50131 then node 3 else 8726
DaveM@34 966 2 class = 8743
DaveM@34 967 3 class = 8726
DaveM@34 968
DaveM@34 969
DaveM@34 970 row =
DaveM@34 971
DaveM@34 972 8925
DaveM@34 973
DaveM@34 974 Row: 8925, pDepth = 4, loss = 0.098551
DaveM@34 975
DaveM@34 976 Decision tree for classification
DaveM@34 977 1 if silence_rate_30dB_mean<0.990566 then node 2 elseif silence_rate_30dB_mean>=0.990566 then node 3 else 8900
DaveM@34 978 2 class = 8900
DaveM@34 979 3 class = 8884
DaveM@34 980
DaveM@34 981
DaveM@34 982 row =
DaveM@34 983
DaveM@34 984 8906
DaveM@34 985
DaveM@34 986 Row: 8906, pDepth = 6, loss = 0.194595
DaveM@34 987
DaveM@34 988 Decision tree for classification
DaveM@34 989 1 if pitch_mean<0.118812 then node 2 elseif pitch_mean>=0.118812 then node 3 else 8856
DaveM@34 990 2 class = 8828
DaveM@34 991 3 class = 8856
DaveM@34 992
DaveM@34 993
DaveM@34 994 row =
DaveM@34 995
DaveM@34 996 8911
DaveM@34 997
DaveM@34 998 Row: 8911, pDepth = 2, loss = 0.075472
DaveM@34 999
DaveM@34 1000 Decision tree for classification
DaveM@34 1001 1 if mfcc_dvar_5<0.228067 then node 2 elseif mfcc_dvar_5>=0.228067 then node 3 else 8829
DaveM@34 1002 2 class = 8829
DaveM@34 1003 3 class = 8821
DaveM@34 1004
DaveM@34 1005
DaveM@34 1006 row =
DaveM@34 1007
DaveM@34 1008 8171
DaveM@34 1009
DaveM@34 1010 Row: 8171, pDepth = 1, loss = 1.000000
DaveM@34 1011
DaveM@34 1012 Decision tree for classification
DaveM@34 1013 1 if mfcc_dvar_5<0.228067 then node 2 elseif mfcc_dvar_5>=0.228067 then node 3 else 8829
DaveM@34 1014 2 class = 8829
DaveM@34 1015 3 class = 8821
DaveM@34 1016
DaveM@34 1017
DaveM@34 1018 row =
DaveM@34 1019
DaveM@34 1020 8380
DaveM@34 1021
DaveM@34 1022 Row: 8380, pDepth = 1, loss = 1.000000
DaveM@34 1023
DaveM@34 1024 Decision tree for classification
DaveM@34 1025 1 if mfcc_dvar_5<0.228067 then node 2 elseif mfcc_dvar_5>=0.228067 then node 3 else 8829
DaveM@34 1026 2 class = 8829
DaveM@34 1027 3 class = 8821
DaveM@34 1028
DaveM@34 1029
DaveM@34 1030 row =
DaveM@34 1031
DaveM@34 1032 8494
DaveM@34 1033
DaveM@34 1034 Row: 8494, pDepth = 2, loss = 0.157895
DaveM@34 1035
DaveM@34 1036 Decision tree for classification
DaveM@34 1037 1 if beats_loudness_band_ratio_mean_3<0.0010675 then node 2 elseif beats_loudness_band_ratio_mean_3>=0.0010675 then node 3 else 7417
DaveM@34 1038 2 class = 7417
DaveM@34 1039 3 class = 7893
DaveM@34 1040
DaveM@34 1041
DaveM@34 1042 row =
DaveM@34 1043
DaveM@34 1044 8759
DaveM@34 1045
DaveM@34 1046 Row: 8759, pDepth = 2, loss = 0.136986
DaveM@34 1047
DaveM@34 1048 Decision tree for classification
DaveM@34 1049 1 if beats_loudness_band_ratio_min_5<0.566381 then node 2 elseif beats_loudness_band_ratio_min_5>=0.566381 then node 3 else 8537
DaveM@34 1050 2 class = 8537
DaveM@34 1051 3 class = 8497
DaveM@34 1052
DaveM@34 1053
DaveM@34 1054 row =
DaveM@34 1055
DaveM@34 1056 8302
DaveM@34 1057
DaveM@34 1058 Row: 8302, pDepth = 2, loss = 0.114286
DaveM@34 1059
DaveM@34 1060 Decision tree for classification
DaveM@34 1061 1 if max_to_total<0.565705 then node 2 elseif max_to_total>=0.565705 then node 3 else 8014
DaveM@34 1062 2 class = 8014
DaveM@34 1063 3 class = 7541
DaveM@34 1064
DaveM@34 1065
DaveM@34 1066 row =
DaveM@34 1067
DaveM@34 1068 8697
DaveM@34 1069
DaveM@34 1070 Row: 8697, pDepth = 1, loss = 0.048193
DaveM@34 1071
DaveM@34 1072 Decision tree for classification
DaveM@34 1073 1 if spectral_energyband_middle_high_mean<7.8e-05 then node 2 elseif spectral_energyband_middle_high_mean>=7.8e-05 then node 3 else 8583
DaveM@34 1074 2 class = 7797
DaveM@34 1075 3 class = 8583
DaveM@34 1076
DaveM@34 1077
DaveM@34 1078 row =
DaveM@34 1079
DaveM@34 1080 8734
DaveM@34 1081
DaveM@34 1082 Row: 8734, pDepth = 1, loss = 1.000000
DaveM@34 1083
DaveM@34 1084 Decision tree for classification
DaveM@34 1085 1 if spectral_energyband_middle_high_mean<7.8e-05 then node 2 elseif spectral_energyband_middle_high_mean>=7.8e-05 then node 3 else 8583
DaveM@34 1086 2 class = 7797
DaveM@34 1087 3 class = 8583
DaveM@34 1088
DaveM@34 1089
DaveM@34 1090 row =
DaveM@34 1091
DaveM@34 1092 8765
DaveM@34 1093
DaveM@34 1094 Row: 8765, pDepth = 2, loss = 0.171875
DaveM@34 1095
DaveM@34 1096 Decision tree for classification
DaveM@34 1097 1 if hfc_mean<0.000376 then node 2 elseif hfc_mean>=0.000376 then node 3 else 8675
DaveM@34 1098 2 class = 8488
DaveM@34 1099 3 class = 8675
DaveM@34 1100
DaveM@34 1101
DaveM@34 1102 row =
DaveM@34 1103
DaveM@34 1104 8362
DaveM@34 1105
DaveM@34 1106 Row: 8362, pDepth = 1, loss = 0.064516
DaveM@34 1107
DaveM@34 1108 Decision tree for classification
DaveM@34 1109 1 if mfcc_median_0<0.194248 then node 2 elseif mfcc_median_0>=0.194248 then node 3 else 8114
DaveM@34 1110 2 class = 8114
DaveM@34 1111 3 class = 7889
DaveM@34 1112
DaveM@34 1113
DaveM@34 1114 row =
DaveM@34 1115
DaveM@34 1116 8886
DaveM@34 1117
DaveM@34 1118 Row: 8886, pDepth = 6, loss = 0.171171
DaveM@34 1119
DaveM@34 1120 Decision tree for classification
DaveM@34 1121 1 if beats_loudness_band_ratio_mean_0<0.147405 then node 2 elseif beats_loudness_band_ratio_mean_0>=0.147405 then node 3 else 8848
DaveM@34 1122 2 class = 8744
DaveM@34 1123 3 if scvalleys_var_5<0.206259 then node 4 elseif scvalleys_var_5>=0.206259 then node 5 else 8848
DaveM@34 1124 4 class = 8744
DaveM@34 1125 5 class = 8848
DaveM@34 1126
DaveM@34 1127
DaveM@34 1128 row =
DaveM@34 1129
DaveM@34 1130 8831
DaveM@34 1131
DaveM@34 1132 Row: 8831, pDepth = 1, loss = 1.000000
DaveM@34 1133
DaveM@34 1134 Decision tree for classification
DaveM@34 1135 1 if beats_loudness_band_ratio_mean_0<0.147405 then node 2 elseif beats_loudness_band_ratio_mean_0>=0.147405 then node 3 else 8848
DaveM@34 1136 2 class = 8744
DaveM@34 1137 3 if scvalleys_var_5<0.206259 then node 4 elseif scvalleys_var_5>=0.206259 then node 5 else 8848
DaveM@34 1138 4 class = 8744
DaveM@34 1139 5 class = 8848
DaveM@34 1140
DaveM@34 1141
DaveM@34 1142 row =
DaveM@34 1143
DaveM@34 1144 8860
DaveM@34 1145
DaveM@34 1146 Row: 8860, pDepth = 6, loss = 0.181347
DaveM@34 1147
DaveM@34 1148 Decision tree for classification
DaveM@34 1149 1 if gfcc_max_0<0.790436 then node 2 elseif gfcc_max_0>=0.790436 then node 3 else 8807
DaveM@34 1150 2 class = 8807
DaveM@34 1151 3 class = 8768
DaveM@34 1152
DaveM@34 1153
DaveM@34 1154 row =
DaveM@34 1155
DaveM@34 1156 8741
DaveM@34 1157
DaveM@34 1158 Row: 8741, pDepth = 2, loss = 0.144444
DaveM@34 1159
DaveM@34 1160 Decision tree for classification
DaveM@34 1161 1 if spectral_flatness_db_dmean2<0.12055 then node 2 elseif spectral_flatness_db_dmean2>=0.12055 then node 3 else 8682
DaveM@34 1162 2 class = 8682
DaveM@34 1163 3 class = 8460
DaveM@34 1164
DaveM@34 1165
DaveM@34 1166 row =
DaveM@34 1167
DaveM@34 1168 8783
DaveM@34 1169
DaveM@34 1170 Row: 8783, pDepth = 2, loss = 0.057377
DaveM@34 1171
DaveM@34 1172 Decision tree for classification
DaveM@34 1173 1 if spectral_entropy_mean<0.717311 then node 2 elseif spectral_entropy_mean>=0.717311 then node 3 else 8698
DaveM@34 1174 2 class = 8075
DaveM@34 1175 3 class = 8698
DaveM@34 1176
DaveM@34 1177
DaveM@34 1178 row =
DaveM@34 1179
DaveM@34 1180 8858
DaveM@34 1181
DaveM@34 1182 Row: 8858, pDepth = 3, loss = 0.099379
DaveM@34 1183
DaveM@34 1184 Decision tree for classification
DaveM@34 1185 1 if beats_loudness_band_ratio_median_0<0.0454905 then node 2 elseif beats_loudness_band_ratio_median_0>=0.0454905 then node 3 else 8755
DaveM@34 1186 2 class = 8755
DaveM@34 1187 3 class = 8711
DaveM@34 1188
DaveM@34 1189
DaveM@34 1190 row =
DaveM@34 1191
DaveM@34 1192 8880
DaveM@34 1193
DaveM@34 1194 Row: 8880, pDepth = 4, loss = 0.116883
DaveM@34 1195
DaveM@34 1196 Decision tree for classification
DaveM@34 1197 1 if spectral_entropy_dmean<0.103471 then node 2 elseif spectral_entropy_dmean>=0.103471 then node 3 else 8833
DaveM@34 1198 2 class = 8341
DaveM@34 1199 3 class = 8833
DaveM@34 1200
DaveM@34 1201
DaveM@34 1202 row =
DaveM@34 1203
DaveM@34 1204 8881
DaveM@34 1205
DaveM@34 1206 Row: 8881, pDepth = 1, loss = 1.000000
DaveM@34 1207
DaveM@34 1208 Decision tree for classification
DaveM@34 1209 1 if spectral_entropy_dmean<0.103471 then node 2 elseif spectral_entropy_dmean>=0.103471 then node 3 else 8833
DaveM@34 1210 2 class = 8341
DaveM@34 1211 3 class = 8833
DaveM@34 1212
DaveM@34 1213
DaveM@34 1214 row =
DaveM@34 1215
DaveM@34 1216 8897
DaveM@34 1217
DaveM@34 1218 Row: 8897, pDepth = 2, loss = 0.135294
DaveM@34 1219
DaveM@34 1220 Decision tree for classification
DaveM@34 1221 1 if frequency_bands_median_16<8.35e-05 then node 2 elseif frequency_bands_median_16>=8.35e-05 then node 3 else 8846
DaveM@34 1222 2 if spectral_energy_var<6.65e-05 then node 4 elseif spectral_energy_var>=6.65e-05 then node 5 else 8846
DaveM@34 1223 3 class = 8784
DaveM@34 1224 4 if frequency_bands_median_16<5e-07 then node 6 elseif frequency_bands_median_16>=5e-07 then node 7 else 8846
DaveM@34 1225 5 if frequency_bands_median_16<5e-07 then node 8 elseif frequency_bands_median_16>=5e-07 then node 9 else 8846
DaveM@34 1226 6 class = 8846
DaveM@34 1227 7 if gfcc_mean_1<0.44926 then node 10 elseif gfcc_mean_1>=0.44926 then node 11 else 8846
DaveM@34 1228 8 if gfcc_mean_1<0.614542 then node 12 elseif gfcc_mean_1>=0.614542 then node 13 else 8846
DaveM@34 1229 9 if frequency_bands_median_16<5.9e-05 then node 14 elseif frequency_bands_median_16>=5.9e-05 then node 15 else 8784
DaveM@34 1230 10 class = 8846
DaveM@34 1231 11 class = 8784
DaveM@34 1232 12 class = 8846
DaveM@34 1233 13 if gfcc_mean_1<0.671312 then node 16 elseif gfcc_mean_1>=0.671312 then node 17 else 8784
DaveM@34 1234 14 if gfcc_mean_1<0.576564 then node 18 elseif gfcc_mean_1>=0.576564 then node 19 else 8784
DaveM@34 1235 15 class = 8846
DaveM@34 1236 16 class = 8784
DaveM@34 1237 17 class = 8846
DaveM@34 1238 18 if gfcc_mean_1<0.361218 then node 20 elseif gfcc_mean_1>=0.361218 then node 21 else 8784
DaveM@34 1239 19 class = 8784
DaveM@34 1240 20 class = 8784
DaveM@34 1241 21 if spectral_energy_var<0.000243 then node 22 elseif spectral_energy_var>=0.000243 then node 23 else 8846
DaveM@34 1242 22 class = 8846
DaveM@34 1243 23 if spectral_energy_var<0.000607 then node 24 elseif spectral_energy_var>=0.000607 then node 25 else 8784
DaveM@34 1244 24 class = 8784
DaveM@34 1245 25 class = 8846
DaveM@34 1246
DaveM@34 1247
DaveM@34 1248 row =
DaveM@34 1249
DaveM@34 1250 8895
DaveM@34 1251
DaveM@34 1252 Row: 8895, pDepth = 4, loss = 0.108225
DaveM@34 1253
DaveM@34 1254 Decision tree for classification
DaveM@34 1255 1 if scvalleys_mean_1<0.706145 then node 2 elseif scvalleys_mean_1>=0.706145 then node 3 else 8840
DaveM@34 1256 2 class = 8864
DaveM@34 1257 3 class = 8840
DaveM@34 1258
DaveM@34 1259
DaveM@34 1260 row =
DaveM@34 1261
DaveM@34 1262 8933
DaveM@34 1263
DaveM@34 1264 Row: 8933, pDepth = 3, loss = 0.051873
DaveM@34 1265
DaveM@34 1266 Decision tree for classification
DaveM@34 1267 1 if silence_rate_60dB_dmean2<0.001268 then node 2 elseif silence_rate_60dB_dmean2>=0.001268 then node 3 else 8872
DaveM@34 1268 2 class = 8838
DaveM@34 1269 3 class = 8872
DaveM@34 1270
DaveM@34 1271
DaveM@34 1272 row =
DaveM@34 1273
DaveM@34 1274 8888
DaveM@34 1275
DaveM@34 1276 Row: 8888, pDepth = 4, loss = 0.144860
DaveM@34 1277
DaveM@34 1278 Decision tree for classification
DaveM@34 1279 1 if spectral_centroid_median<0.248646 then node 2 elseif spectral_centroid_median>=0.248646 then node 3 else 8816
DaveM@34 1280 2 class = 8816
DaveM@34 1281 3 class = 8764
DaveM@34 1282
DaveM@34 1283
DaveM@34 1284 row =
DaveM@34 1285
DaveM@34 1286 8905
DaveM@34 1287
DaveM@34 1288 Row: 8905, pDepth = 5, loss = 0.124031
DaveM@34 1289
DaveM@34 1290 Decision tree for classification
DaveM@34 1291 1 if spectral_flatness_db_mean<0.320336 then node 2 elseif spectral_flatness_db_mean>=0.320336 then node 3 else 8868
DaveM@34 1292 2 class = 8868
DaveM@34 1293 3 class = 8738
DaveM@34 1294
DaveM@34 1295
DaveM@34 1296 row =
DaveM@34 1297
DaveM@34 1298 8893
DaveM@34 1299
DaveM@34 1300 Row: 8893, pDepth = 3, loss = 0.096939
DaveM@34 1301
DaveM@34 1302 Decision tree for classification
DaveM@34 1303 1 if frequency_bands_median_21<0.0001485 then node 2 elseif frequency_bands_median_21>=0.0001485 then node 3 else 8785
DaveM@34 1304 2 class = 8785
DaveM@34 1305 3 class = 8678
DaveM@34 1306
DaveM@34 1307
DaveM@34 1308 row =
DaveM@34 1309
DaveM@34 1310 8940
DaveM@34 1311
DaveM@34 1312 Row: 8940, pDepth = 9, loss = 0.172619
DaveM@34 1313
DaveM@34 1314 Decision tree for classification
DaveM@34 1315 1 if beats_loudness_band_ratio_mean_5<0.0182555 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.0182555 then node 3 else 8912
DaveM@34 1316 2 class = 8912
DaveM@34 1317 3 class = 8876
DaveM@34 1318
DaveM@34 1319
DaveM@34 1320 row =
DaveM@34 1321
DaveM@34 1322 8750
DaveM@34 1323
DaveM@34 1324 Row: 8750, pDepth = 3, loss = 0.172727
DaveM@34 1325
DaveM@34 1326 Decision tree for classification
DaveM@34 1327 1 if first_peak_weight_mean<0.763889 then node 2 elseif first_peak_weight_mean>=0.763889 then node 3 else 8464
DaveM@34 1328 2 class = 8720
DaveM@34 1329 3 class = 8464
DaveM@34 1330
DaveM@34 1331
DaveM@34 1332 row =
DaveM@34 1333
DaveM@34 1334 8902
DaveM@34 1335
DaveM@34 1336 Row: 8902, pDepth = 2, loss = 0.107914
DaveM@34 1337
DaveM@34 1338 Decision tree for classification
DaveM@34 1339 1 if scvalleys_max_2<0.700581 then node 2 elseif scvalleys_max_2>=0.700581 then node 3 else 8824
DaveM@34 1340 2 class = 8790
DaveM@34 1341 3 class = 8824
DaveM@34 1342
DaveM@34 1343
DaveM@34 1344 row =
DaveM@34 1345
DaveM@34 1346 8935
DaveM@34 1347
DaveM@34 1348 Row: 8935, pDepth = 1, loss = 0.042781
DaveM@34 1349
DaveM@34 1350 Decision tree for classification
DaveM@34 1351 1 if spectral_entropy_max<0.91212 then node 2 elseif spectral_entropy_max>=0.91212 then node 3 else 8871
DaveM@34 1352 2 class = 8835
DaveM@34 1353 3 class = 8871
DaveM@34 1354
DaveM@34 1355
DaveM@34 1356 row =
DaveM@34 1357
DaveM@34 1358 8955
DaveM@34 1359
DaveM@34 1360 Row: 8955, pDepth = 7, loss = 0.140684
DaveM@34 1361
DaveM@34 1362 Decision tree for classification
DaveM@34 1363 1 if second_peak_weight_median<0.17782 then node 2 elseif second_peak_weight_median>=0.17782 then node 3 else 8910
DaveM@34 1364 2 class = 8910
DaveM@34 1365 3 class = 8946
DaveM@34 1366
DaveM@34 1367
DaveM@34 1368 row =
DaveM@34 1369
DaveM@34 1370 8915
DaveM@34 1371
DaveM@34 1372 Row: 8915, pDepth = 6, loss = 0.083056
DaveM@34 1373
DaveM@34 1374 Decision tree for classification
DaveM@34 1375 1 if max_der_before_max_mean<0.549144 then node 2 elseif max_der_before_max_mean>=0.549144 then node 3 else 8837
DaveM@34 1376 2 class = 8819
DaveM@34 1377 3 class = 8837
DaveM@34 1378
DaveM@34 1379
DaveM@34 1380 row =
DaveM@34 1381
DaveM@34 1382 8920
DaveM@34 1383
DaveM@34 1384 Row: 8920, pDepth = 4, loss = 0.133588
DaveM@34 1385
DaveM@34 1386 Decision tree for classification
DaveM@34 1387 1 if barkbands_max_18<0.0004445 then node 2 elseif barkbands_max_18>=0.0004445 then node 3 else 8891
DaveM@34 1388 2 class = 8843
DaveM@34 1389 3 class = 8891
DaveM@34 1390
DaveM@34 1391
DaveM@34 1392 row =
DaveM@34 1393
DaveM@34 1394 8919
DaveM@34 1395
DaveM@34 1396 Row: 8919, pDepth = 1, loss = 0.072131
DaveM@34 1397
DaveM@34 1398 Decision tree for classification
DaveM@34 1399 1 if first_peak_weight_mean<0.763889 then node 2 elseif first_peak_weight_mean>=0.763889 then node 3 else 8873
DaveM@34 1400 2 class = 8852
DaveM@34 1401 3 class = 8873
DaveM@34 1402
DaveM@34 1403
DaveM@34 1404 row =
DaveM@34 1405
DaveM@34 1406 8936
DaveM@34 1407
DaveM@34 1408 Row: 8936, pDepth = 6, loss = 0.074786
DaveM@34 1409
DaveM@34 1410 Decision tree for classification
DaveM@34 1411 1 if beats_loudness_band_ratio_max_0<0.714803 then node 2 elseif beats_loudness_band_ratio_max_0>=0.714803 then node 3 else 8921
DaveM@34 1412 2 class = 8921
DaveM@34 1413 3 class = 8857
DaveM@34 1414
DaveM@34 1415
DaveM@34 1416 row =
DaveM@34 1417
DaveM@34 1418 8707
DaveM@34 1419
DaveM@34 1420 Row: 8707, pDepth = 1, loss = 0.022222
DaveM@34 1421
DaveM@34 1422 Decision tree for classification
DaveM@34 1423 1 if second_peak_spread_max<0.050472 then node 2 elseif second_peak_spread_max>=0.050472 then node 3 else 8301
DaveM@34 1424 2 class = 8156
DaveM@34 1425 3 class = 8301
DaveM@34 1426
DaveM@34 1427
DaveM@34 1428 row =
DaveM@34 1429
DaveM@34 1430 8760
DaveM@34 1431
DaveM@34 1432 Row: 8760, pDepth = 2, loss = 0.109756
DaveM@34 1433
DaveM@34 1434 Decision tree for classification
DaveM@34 1435 1 if barkbands_dmean2_11<7.5e-06 then node 2 elseif barkbands_dmean2_11>=7.5e-06 then node 3 else 8063
DaveM@34 1436 2 class = 8327
DaveM@34 1437 3 class = 8063
DaveM@34 1438
DaveM@34 1439
DaveM@34 1440 row =
DaveM@34 1441
DaveM@34 1442 8918
DaveM@34 1443
DaveM@34 1444 Row: 8918, pDepth = 7, loss = 0.181818
DaveM@34 1445
DaveM@34 1446 Decision tree for classification
DaveM@34 1447 1 if beats_loudness_band_ratio_max_5<0.0010565 then node 2 elseif beats_loudness_band_ratio_max_5>=0.0010565 then node 3 else 8869
DaveM@34 1448 2 class = 8883
DaveM@34 1449 3 class = 8869
DaveM@34 1450
DaveM@34 1451
DaveM@34 1452 row =
DaveM@34 1453
DaveM@34 1454 8932
DaveM@34 1455
DaveM@34 1456 Row: 8932, pDepth = 3, loss = 0.052885
DaveM@34 1457
DaveM@34 1458 Decision tree for classification
DaveM@34 1459 1 if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
DaveM@34 1460 2 class = 8894
DaveM@34 1461 3 class = 8908
DaveM@34 1462
DaveM@34 1463
DaveM@34 1464 row =
DaveM@34 1465
DaveM@34 1466 8540
DaveM@34 1467
DaveM@34 1468 Row: 8540, pDepth = 1, loss = 1.000000
DaveM@34 1469
DaveM@34 1470 Decision tree for classification
DaveM@34 1471 1 if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
DaveM@34 1472 2 class = 8894
DaveM@34 1473 3 class = 8908
DaveM@34 1474
DaveM@34 1475
DaveM@34 1476 row =
DaveM@34 1477
DaveM@34 1478 8677
DaveM@34 1479
DaveM@34 1480 Row: 8677, pDepth = 1, loss = 1.000000
DaveM@34 1481
DaveM@34 1482 Decision tree for classification
DaveM@34 1483 1 if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
DaveM@34 1484 2 class = 8894
DaveM@34 1485 3 class = 8908
DaveM@34 1486
DaveM@34 1487
DaveM@34 1488 row =
DaveM@34 1489
DaveM@34 1490 8474
DaveM@34 1491
DaveM@34 1492 Row: 8474, pDepth = 0, loss = 1.000000
DaveM@34 1493
DaveM@34 1494 Decision tree for classification
DaveM@34 1495 1 if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
DaveM@34 1496 2 class = 8894
DaveM@34 1497 3 class = 8908
DaveM@34 1498
DaveM@34 1499
DaveM@34 1500 row =
DaveM@34 1501
DaveM@34 1502 8890
DaveM@34 1503
DaveM@34 1504 Row: 8890, pDepth = 1, loss = 1.000000
DaveM@34 1505
DaveM@34 1506 Decision tree for classification
DaveM@34 1507 1 if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
DaveM@34 1508 2 class = 8894
DaveM@34 1509 3 class = 8908
DaveM@34 1510
DaveM@34 1511
DaveM@34 1512 row =
DaveM@34 1513
DaveM@34 1514 8424
DaveM@34 1515
DaveM@34 1516 Row: 8424, pDepth = 0, loss = 1.000000
DaveM@34 1517
DaveM@34 1518 Decision tree for classification
DaveM@34 1519 1 if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
DaveM@34 1520 2 class = 8894
DaveM@34 1521 3 class = 8908
DaveM@34 1522
DaveM@34 1523
DaveM@34 1524 row =
DaveM@34 1525
DaveM@34 1526 8127
DaveM@34 1527
DaveM@34 1528 Row: 8127, pDepth = 1, loss = 1.000000
DaveM@34 1529
DaveM@34 1530 Decision tree for classification
DaveM@34 1531 1 if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
DaveM@34 1532 2 class = 8894
DaveM@34 1533 3 class = 8908
DaveM@34 1534
DaveM@34 1535
DaveM@34 1536 row =
DaveM@34 1537
DaveM@34 1538 8938
DaveM@34 1539
DaveM@34 1540 Row: 8938, pDepth = 3, loss = 0.105000
DaveM@34 1541
DaveM@34 1542 Decision tree for classification
DaveM@34 1543 1 if inharmonicity_median<0.00494 then node 2 elseif inharmonicity_median>=0.00494 then node 3 else 8896
DaveM@34 1544 2 class = 8927
DaveM@34 1545 3 class = 8896
DaveM@34 1546
DaveM@34 1547
DaveM@34 1548 row =
DaveM@34 1549
DaveM@34 1550 8941
DaveM@34 1551
DaveM@34 1552 Row: 8941, pDepth = 1, loss = 0.038610
DaveM@34 1553
DaveM@34 1554 Decision tree for classification
DaveM@34 1555 1 if gfcc_mean_1<0.300223 then node 2 elseif gfcc_mean_1>=0.300223 then node 3 else 8882
DaveM@34 1556 2 class = 8767
DaveM@34 1557 3 class = 8882
DaveM@34 1558
DaveM@34 1559
DaveM@34 1560 row =
DaveM@34 1561
DaveM@34 1562 8924
DaveM@34 1563
DaveM@34 1564 Row: 8924, pDepth = 1, loss = 0.066845
DaveM@34 1565
DaveM@34 1566 Decision tree for classification
DaveM@34 1567 1 if first_peak_spread_median<0.215852 then node 2 elseif first_peak_spread_median>=0.215852 then node 3 else 8914
DaveM@34 1568 2 class = 8914
DaveM@34 1569 3 class = 8781
DaveM@34 1570
DaveM@34 1571
DaveM@34 1572 row =
DaveM@34 1573
DaveM@34 1574 8929
DaveM@34 1575
DaveM@34 1576 Row: 8929, pDepth = 7, loss = 0.187013
DaveM@34 1577
DaveM@34 1578 Decision tree for classification
DaveM@34 1579 1 if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
DaveM@34 1580 2 class = 8916
DaveM@34 1581 3 class = 8823
DaveM@34 1582
DaveM@34 1583
DaveM@34 1584 row =
DaveM@34 1585
DaveM@34 1586 7850
DaveM@34 1587
DaveM@34 1588 Row: 7850, pDepth = 0, loss = 1.000000
DaveM@34 1589
DaveM@34 1590 Decision tree for classification
DaveM@34 1591 1 if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
DaveM@34 1592 2 class = 8916
DaveM@34 1593 3 class = 8823
DaveM@34 1594
DaveM@34 1595
DaveM@34 1596 row =
DaveM@34 1597
DaveM@34 1598 7940
DaveM@34 1599
DaveM@34 1600 Row: 7940, pDepth = 0, loss = 1.000000
DaveM@34 1601
DaveM@34 1602 Decision tree for classification
DaveM@34 1603 1 if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
DaveM@34 1604 2 class = 8916
DaveM@34 1605 3 class = 8823
DaveM@34 1606
DaveM@34 1607
DaveM@34 1608 row =
DaveM@34 1609
DaveM@34 1610 8371
DaveM@34 1611
DaveM@34 1612 Row: 8371, pDepth = 0, loss = 1.000000
DaveM@34 1613
DaveM@34 1614 Decision tree for classification
DaveM@34 1615 1 if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
DaveM@34 1616 2 class = 8916
DaveM@34 1617 3 class = 8823
DaveM@34 1618
DaveM@34 1619
DaveM@34 1620 row =
DaveM@34 1621
DaveM@34 1622 8530
DaveM@34 1623
DaveM@34 1624 Row: 8530, pDepth = 0, loss = 1.000000
DaveM@34 1625
DaveM@34 1626 Decision tree for classification
DaveM@34 1627 1 if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
DaveM@34 1628 2 class = 8916
DaveM@34 1629 3 class = 8823
DaveM@34 1630
DaveM@34 1631
DaveM@34 1632 row =
DaveM@34 1633
DaveM@34 1634 6907
DaveM@34 1635
DaveM@34 1636 Row: 6907, pDepth = 0, loss = 1.000000
DaveM@34 1637
DaveM@34 1638 Decision tree for classification
DaveM@34 1639 1 if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
DaveM@34 1640 2 class = 8916
DaveM@34 1641 3 class = 8823
DaveM@34 1642
DaveM@34 1643
DaveM@34 1644 row =
DaveM@34 1645
DaveM@34 1646 7646
DaveM@34 1647
DaveM@34 1648 Row: 7646, pDepth = 1, loss = 1.000000
DaveM@34 1649
DaveM@34 1650 Decision tree for classification
DaveM@34 1651 1 if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
DaveM@34 1652 2 class = 8916
DaveM@34 1653 3 class = 8823
DaveM@34 1654
DaveM@34 1655
DaveM@34 1656 row =
DaveM@34 1657
DaveM@34 1658 8606
DaveM@34 1659
DaveM@34 1660 Row: 8606, pDepth = 1, loss = 0.066667
DaveM@34 1661
DaveM@34 1662 Decision tree for classification
DaveM@34 1663 1 if frequency_bands_min_6<1.5e-06 then node 2 elseif frequency_bands_min_6>=1.5e-06 then node 3 else 8276
DaveM@34 1664 2 class = 8492
DaveM@34 1665 3 class = 8276
DaveM@34 1666
DaveM@34 1667
DaveM@34 1668 row =
DaveM@34 1669
DaveM@34 1670 8650
DaveM@34 1671
DaveM@34 1672 Row: 8650, pDepth = 1, loss = 1.000000
DaveM@34 1673
DaveM@34 1674 Decision tree for classification
DaveM@34 1675 1 if frequency_bands_min_6<1.5e-06 then node 2 elseif frequency_bands_min_6>=1.5e-06 then node 3 else 8276
DaveM@34 1676 2 class = 8492
DaveM@34 1677 3 class = 8276
DaveM@34 1678
DaveM@34 1679
DaveM@34 1680 row =
DaveM@34 1681
DaveM@34 1682 7144
DaveM@34 1683
DaveM@34 1684 Row: 7144, pDepth = 0, loss = 1.000000
DaveM@34 1685
DaveM@34 1686 Decision tree for classification
DaveM@34 1687 1 if frequency_bands_min_6<1.5e-06 then node 2 elseif frequency_bands_min_6>=1.5e-06 then node 3 else 8276
DaveM@34 1688 2 class = 8492
DaveM@34 1689 3 class = 8276
DaveM@34 1690
DaveM@34 1691
DaveM@34 1692 row =
DaveM@34 1693
DaveM@34 1694 8230
DaveM@34 1695
DaveM@34 1696 Row: 8230, pDepth = 0, loss = 1.000000
DaveM@34 1697
DaveM@34 1698 Decision tree for classification
DaveM@34 1699 1 if frequency_bands_min_6<1.5e-06 then node 2 elseif frequency_bands_min_6>=1.5e-06 then node 3 else 8276
DaveM@34 1700 2 class = 8492
DaveM@34 1701 3 class = 8276
DaveM@34 1702
DaveM@34 1703
DaveM@34 1704 row =
DaveM@34 1705
DaveM@34 1706 8471
DaveM@34 1707
DaveM@34 1708 Row: 8471, pDepth = 1, loss = 1.000000
DaveM@34 1709
DaveM@34 1710 Decision tree for classification
DaveM@34 1711 1 if frequency_bands_min_6<1.5e-06 then node 2 elseif frequency_bands_min_6>=1.5e-06 then node 3 else 8276
DaveM@34 1712 2 class = 8492
DaveM@34 1713 3 class = 8276
DaveM@34 1714
DaveM@34 1715
DaveM@34 1716 row =
DaveM@34 1717
DaveM@34 1718 8430
DaveM@34 1719
DaveM@34 1720 Row: 8430, pDepth = 0, loss = 1.000000
DaveM@34 1721
DaveM@34 1722 Decision tree for classification
DaveM@34 1723 1 if frequency_bands_min_6<1.5e-06 then node 2 elseif frequency_bands_min_6>=1.5e-06 then node 3 else 8276
DaveM@34 1724 2 class = 8492
DaveM@34 1725 3 class = 8276
DaveM@34 1726
DaveM@34 1727
DaveM@34 1728 row =
DaveM@34 1729
DaveM@34 1730 8633
DaveM@34 1731
DaveM@34 1732 Row: 8633, pDepth = 1, loss = 0.127660
DaveM@34 1733
DaveM@34 1734 Decision tree for classification
DaveM@34 1735 1 if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
DaveM@34 1736 2 class = 8387
DaveM@34 1737 3 class = 8563
DaveM@34 1738
DaveM@34 1739
DaveM@34 1740 row =
DaveM@34 1741
DaveM@34 1742 8217
DaveM@34 1743
DaveM@34 1744 Row: 8217, pDepth = 1, loss = 1.000000
DaveM@34 1745
DaveM@34 1746 Decision tree for classification
DaveM@34 1747 1 if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
DaveM@34 1748 2 class = 8387
DaveM@34 1749 3 class = 8563
DaveM@34 1750
DaveM@34 1751
DaveM@34 1752 row =
DaveM@34 1753
DaveM@34 1754 8534
DaveM@34 1755
DaveM@34 1756 Row: 8534, pDepth = 0, loss = 1.000000
DaveM@34 1757
DaveM@34 1758 Decision tree for classification
DaveM@34 1759 1 if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
DaveM@34 1760 2 class = 8387
DaveM@34 1761 3 class = 8563
DaveM@34 1762
DaveM@34 1763
DaveM@34 1764 row =
DaveM@34 1765
DaveM@34 1766 712
DaveM@34 1767
DaveM@34 1768 Row: 712, pDepth = 0, loss = 1.000000
DaveM@34 1769
DaveM@34 1770 Decision tree for classification
DaveM@34 1771 1 if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
DaveM@34 1772 2 class = 8387
DaveM@34 1773 3 class = 8563
DaveM@34 1774
DaveM@34 1775
DaveM@34 1776 row =
DaveM@34 1777
DaveM@34 1778 2269
DaveM@34 1779
DaveM@34 1780 Row: 2269, pDepth = 0, loss = 1.000000
DaveM@34 1781
DaveM@34 1782 Decision tree for classification
DaveM@34 1783 1 if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
DaveM@34 1784 2 class = 8387
DaveM@34 1785 3 class = 8563
DaveM@34 1786
DaveM@34 1787
DaveM@34 1788 row =
DaveM@34 1789
DaveM@34 1790 4426
DaveM@34 1791
DaveM@34 1792 Row: 4426, pDepth = 0, loss = 1.000000
DaveM@34 1793
DaveM@34 1794 Decision tree for classification
DaveM@34 1795 1 if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
DaveM@34 1796 2 class = 8387
DaveM@34 1797 3 class = 8563
DaveM@34 1798
DaveM@34 1799
DaveM@34 1800 row =
DaveM@34 1801
DaveM@34 1802 5110
DaveM@34 1803
DaveM@34 1804 Row: 5110, pDepth = 0, loss = 1.000000
DaveM@34 1805
DaveM@34 1806 Decision tree for classification
DaveM@34 1807 1 if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
DaveM@34 1808 2 class = 8387
DaveM@34 1809 3 class = 8563
DaveM@34 1810
DaveM@34 1811
DaveM@34 1812 row =
DaveM@34 1813
DaveM@34 1814 8312
DaveM@34 1815
DaveM@34 1816 Row: 8312, pDepth = 1, loss = 1.000000
DaveM@34 1817
DaveM@34 1818 Decision tree for classification
DaveM@34 1819 1 if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
DaveM@34 1820 2 class = 8387
DaveM@34 1821 3 class = 8563
DaveM@34 1822
DaveM@34 1823
DaveM@34 1824 row =
DaveM@34 1825
DaveM@34 1826 8621
DaveM@34 1827
DaveM@34 1828 Row: 8621, pDepth = 1, loss = 1.000000
DaveM@34 1829
DaveM@34 1830 Decision tree for classification
DaveM@34 1831 1 if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
DaveM@34 1832 2 class = 8387
DaveM@34 1833 3 class = 8563
DaveM@34 1834
DaveM@34 1835
DaveM@34 1836 row =
DaveM@34 1837
DaveM@34 1838 8799
DaveM@34 1839
DaveM@34 1840 Row: 8799, pDepth = 1, loss = 0.145455
DaveM@34 1841
DaveM@34 1842 Decision tree for classification
DaveM@34 1843 1 if mfcc_var_0<0.25508 then node 2 elseif mfcc_var_0>=0.25508 then node 3 else 8719
DaveM@34 1844 2 class = 8719
DaveM@34 1845 3 class = 8645
DaveM@34 1846
DaveM@34 1847
DaveM@34 1848 row =
DaveM@34 1849
DaveM@34 1850 8870
DaveM@34 1851
DaveM@34 1852 Row: 8870, pDepth = 2, loss = 0.128571
DaveM@34 1853
DaveM@34 1854 Decision tree for classification
DaveM@34 1855 1 if spectral_flatness_db_dmean2<0.165319 then node 2 elseif spectral_flatness_db_dmean2>=0.165319 then node 3 else 8811
DaveM@34 1856 2 class = 8811
DaveM@34 1857 3 class = 8791
DaveM@34 1858
DaveM@34 1859
DaveM@34 1860 row =
DaveM@34 1861
DaveM@34 1862 8726
DaveM@34 1863
DaveM@34 1864 Row: 8726, pDepth = 1, loss = 0.132075
DaveM@34 1865
DaveM@34 1866 Decision tree for classification
DaveM@34 1867 1 if spectral_spread_var<0.107504 then node 2 elseif spectral_spread_var>=0.107504 then node 3 else 8431
DaveM@34 1868 2 class = 8515
DaveM@34 1869 3 class = 8431
DaveM@34 1870
DaveM@34 1871
DaveM@34 1872 row =
DaveM@34 1873
DaveM@34 1874 8743
DaveM@34 1875
DaveM@34 1876 Row: 8743, pDepth = 1, loss = 1.000000
DaveM@34 1877
DaveM@34 1878 Decision tree for classification
DaveM@34 1879 1 if spectral_spread_var<0.107504 then node 2 elseif spectral_spread_var>=0.107504 then node 3 else 8431
DaveM@34 1880 2 class = 8515
DaveM@34 1881 3 class = 8431
DaveM@34 1882
DaveM@34 1883
DaveM@34 1884 row =
DaveM@34 1885
DaveM@34 1886 8884
DaveM@34 1887
DaveM@34 1888 Row: 8884, pDepth = 5, loss = 0.149351
DaveM@34 1889
DaveM@34 1890 Decision tree for classification
DaveM@34 1891 1 if gfcc_mean_0<0.755376 then node 2 elseif gfcc_mean_0>=0.755376 then node 3 else 8788
DaveM@34 1892 2 class = 8788
DaveM@34 1893 3 class = 8826
DaveM@34 1894
DaveM@34 1895
DaveM@34 1896 row =
DaveM@34 1897
DaveM@34 1898 8900
DaveM@34 1899
DaveM@34 1900 Row: 8900, pDepth = 6, loss = 0.125654
DaveM@34 1901
DaveM@34 1902 Decision tree for classification
DaveM@34 1903 1 if barkbands_median_20<0.0007535 then node 2 elseif barkbands_median_20>=0.0007535 then node 3 else 8861
DaveM@34 1904 2 class = 8861
DaveM@34 1905 3 class = 8721
DaveM@34 1906
DaveM@34 1907
DaveM@34 1908 row =
DaveM@34 1909
DaveM@34 1910 8828
DaveM@34 1911
DaveM@34 1912 Row: 8828, pDepth = 1, loss = 0.065934
DaveM@34 1913
DaveM@34 1914 Decision tree for classification
DaveM@34 1915 1 if beats_loudness_band_ratio_max_0<0.40722 then node 2 elseif beats_loudness_band_ratio_max_0>=0.40722 then node 3 else 8786
DaveM@34 1916 2 class = 8786
DaveM@34 1917 3 class = 8761
DaveM@34 1918
DaveM@34 1919
DaveM@34 1920 row =
DaveM@34 1921
DaveM@34 1922 8856
DaveM@34 1923
DaveM@34 1924 Row: 8856, pDepth = 3, loss = 0.095745
DaveM@34 1925
DaveM@34 1926 Decision tree for classification
DaveM@34 1927 1 if inharmonicity_mean<0.157454 then node 2 elseif inharmonicity_mean>=0.157454 then node 3 else 8731
DaveM@34 1928 2 class = 8690
DaveM@34 1929 3 class = 8731
DaveM@34 1930
DaveM@34 1931
DaveM@34 1932 row =
DaveM@34 1933
DaveM@34 1934 8821
DaveM@34 1935
DaveM@34 1936 Row: 8821, pDepth = 1, loss = 1.000000
DaveM@34 1937
DaveM@34 1938 Decision tree for classification
DaveM@34 1939 1 if inharmonicity_mean<0.157454 then node 2 elseif inharmonicity_mean>=0.157454 then node 3 else 8731
DaveM@34 1940 2 class = 8690
DaveM@34 1941 3 class = 8731
DaveM@34 1942
DaveM@34 1943
DaveM@34 1944 row =
DaveM@34 1945
DaveM@34 1946 8829
DaveM@34 1947
DaveM@34 1948 Row: 8829, pDepth = 1, loss = 0.031250
DaveM@34 1949
DaveM@34 1950 Decision tree for classification
DaveM@34 1951 1 if tristimulus_mean_1<0.160731 then node 2 elseif tristimulus_mean_1>=0.160731 then node 3 else 8725
DaveM@34 1952 2 class = 8725
DaveM@34 1953 3 class = 8619
DaveM@34 1954
DaveM@34 1955
DaveM@34 1956 row =
DaveM@34 1957
DaveM@34 1958 6751
DaveM@34 1959
DaveM@34 1960 Row: 6751, pDepth = 0, loss = 1.000000
DaveM@34 1961
DaveM@34 1962 Decision tree for classification
DaveM@34 1963 1 if tristimulus_mean_1<0.160731 then node 2 elseif tristimulus_mean_1>=0.160731 then node 3 else 8725
DaveM@34 1964 2 class = 8725
DaveM@34 1965 3 class = 8619
DaveM@34 1966
DaveM@34 1967
DaveM@34 1968 row =
DaveM@34 1969
DaveM@34 1970 8021
DaveM@34 1971
DaveM@34 1972 Row: 8021, pDepth = 0, loss = 1.000000
DaveM@34 1973
DaveM@34 1974 Decision tree for classification
DaveM@34 1975 1 if tristimulus_mean_1<0.160731 then node 2 elseif tristimulus_mean_1>=0.160731 then node 3 else 8725
DaveM@34 1976 2 class = 8725
DaveM@34 1977 3 class = 8619
DaveM@34 1978
DaveM@34 1979
DaveM@34 1980 row =
DaveM@34 1981
DaveM@34 1982 7998
DaveM@34 1983
DaveM@34 1984 Row: 7998, pDepth = 0, loss = 1.000000
DaveM@34 1985
DaveM@34 1986 Decision tree for classification
DaveM@34 1987 1 if tristimulus_mean_1<0.160731 then node 2 elseif tristimulus_mean_1>=0.160731 then node 3 else 8725
DaveM@34 1988 2 class = 8725
DaveM@34 1989 3 class = 8619
DaveM@34 1990
DaveM@34 1991
DaveM@34 1992 row =
DaveM@34 1993
DaveM@34 1994 8275
DaveM@34 1995
DaveM@34 1996 Row: 8275, pDepth = 1, loss = 1.000000
DaveM@34 1997
DaveM@34 1998 Decision tree for classification
DaveM@34 1999 1 if tristimulus_mean_1<0.160731 then node 2 elseif tristimulus_mean_1>=0.160731 then node 3 else 8725
DaveM@34 2000 2 class = 8725
DaveM@34 2001 3 class = 8619
DaveM@34 2002
DaveM@34 2003
DaveM@34 2004 row =
DaveM@34 2005
DaveM@34 2006 7417
DaveM@34 2007
DaveM@34 2008 Row: 7417, pDepth = 1, loss = 0.060606
DaveM@34 2009
DaveM@34 2010 Decision tree for classification
DaveM@34 2011 1 if spectral_entropy_dmean2<0.119373 then node 2 elseif spectral_entropy_dmean2>=0.119373 then node 3 else 6660
DaveM@34 2012 2 class = 6662
DaveM@34 2013 3 class = 6660
DaveM@34 2014
DaveM@34 2015
DaveM@34 2016 row =
DaveM@34 2017
DaveM@34 2018 7893
DaveM@34 2019
DaveM@34 2020 Row: 7893, pDepth = 1, loss = 1.000000
DaveM@34 2021
DaveM@34 2022 Decision tree for classification
DaveM@34 2023 1 if spectral_entropy_dmean2<0.119373 then node 2 elseif spectral_entropy_dmean2>=0.119373 then node 3 else 6660
DaveM@34 2024 2 class = 6662
DaveM@34 2025 3 class = 6660
DaveM@34 2026
DaveM@34 2027
DaveM@34 2028 row =
DaveM@34 2029
DaveM@34 2030 8497
DaveM@34 2031
DaveM@34 2032 Row: 8497, pDepth = 1, loss = 0.074074
DaveM@34 2033
DaveM@34 2034 Decision tree for classification
DaveM@34 2035 1 if spectral_rms_mean<0.026824 then node 2 elseif spectral_rms_mean>=0.026824 then node 3 else 7955
DaveM@34 2036 2 class = 7702
DaveM@34 2037 3 class = 7955
DaveM@34 2038
DaveM@34 2039
DaveM@34 2040 row =
DaveM@34 2041
DaveM@34 2042 8537
DaveM@34 2043
DaveM@34 2044 Row: 8537, pDepth = 2, loss = 0.108696
DaveM@34 2045
DaveM@34 2046 Decision tree for classification
DaveM@34 2047 1 if spectral_entropy_min<0.583247 then node 2 elseif spectral_entropy_min>=0.583247 then node 3 else 8179
DaveM@34 2048 2 class = 8069
DaveM@34 2049 3 class = 8179
DaveM@34 2050
DaveM@34 2051
DaveM@34 2052 row =
DaveM@34 2053
DaveM@34 2054 7541
DaveM@34 2055
DaveM@34 2056 Row: 7541, pDepth = 1, loss = 1.000000
DaveM@34 2057
DaveM@34 2058 Decision tree for classification
DaveM@34 2059 1 if spectral_entropy_min<0.583247 then node 2 elseif spectral_entropy_min>=0.583247 then node 3 else 8179
DaveM@34 2060 2 class = 8069
DaveM@34 2061 3 class = 8179
DaveM@34 2062
DaveM@34 2063
DaveM@34 2064 row =
DaveM@34 2065
DaveM@34 2066 8014
DaveM@34 2067
DaveM@34 2068 Row: 8014, pDepth = 1, loss = 1.000000
DaveM@34 2069
DaveM@34 2070 Decision tree for classification
DaveM@34 2071 1 if spectral_entropy_min<0.583247 then node 2 elseif spectral_entropy_min>=0.583247 then node 3 else 8179
DaveM@34 2072 2 class = 8069
DaveM@34 2073 3 class = 8179
DaveM@34 2074
DaveM@34 2075
DaveM@34 2076 row =
DaveM@34 2077
DaveM@34 2078 7797
DaveM@34 2079
DaveM@34 2080 Row: 7797, pDepth = 1, loss = 1.000000
DaveM@34 2081
DaveM@34 2082 Decision tree for classification
DaveM@34 2083 1 if spectral_entropy_min<0.583247 then node 2 elseif spectral_entropy_min>=0.583247 then node 3 else 8179
DaveM@34 2084 2 class = 8069
DaveM@34 2085 3 class = 8179
DaveM@34 2086
DaveM@34 2087
DaveM@34 2088 row =
DaveM@34 2089
DaveM@34 2090 8583
DaveM@34 2091
DaveM@34 2092 Row: 8583, pDepth = 1, loss = 0.098592
DaveM@34 2093
DaveM@34 2094 Decision tree for classification
DaveM@34 2095 1 if zerocrossingrate_min<0.0510405 then node 2 elseif zerocrossingrate_min>=0.0510405 then node 3 else 8289
DaveM@34 2096 2 class = 8245
DaveM@34 2097 3 class = 8289
DaveM@34 2098
DaveM@34 2099
DaveM@34 2100 row =
DaveM@34 2101
DaveM@34 2102 7922
DaveM@34 2103
DaveM@34 2104 Row: 7922, pDepth = 0, loss = 1.000000
DaveM@34 2105
DaveM@34 2106 Decision tree for classification
DaveM@34 2107 1 if zerocrossingrate_min<0.0510405 then node 2 elseif zerocrossingrate_min>=0.0510405 then node 3 else 8289
DaveM@34 2108 2 class = 8245
DaveM@34 2109 3 class = 8289
DaveM@34 2110
DaveM@34 2111
DaveM@34 2112 row =
DaveM@34 2113
DaveM@34 2114 8708
DaveM@34 2115
DaveM@34 2116 Row: 8708, pDepth = 2, loss = 0.053571
DaveM@34 2117
DaveM@34 2118 Decision tree for classification
DaveM@34 2119 1 if beats_loudness_band_ratio_max_0<0.012433 then node 2 elseif beats_loudness_band_ratio_max_0>=0.012433 then node 3 else 8370
DaveM@34 2120 2 class = 8469
DaveM@34 2121 3 class = 8370
DaveM@34 2122
DaveM@34 2123
DaveM@34 2124 row =
DaveM@34 2125
DaveM@34 2126 8488
DaveM@34 2127
DaveM@34 2128 Row: 8488, pDepth = 1, loss = 0.132075
DaveM@34 2129
DaveM@34 2130 Decision tree for classification
DaveM@34 2131 1 if scvalleys_dvar_1<0.0257925 then node 2 elseif scvalleys_dvar_1>=0.0257925 then node 3 else 8201
DaveM@34 2132 2 class = 8201
DaveM@34 2133 3 class = 8393
DaveM@34 2134
DaveM@34 2135
DaveM@34 2136 row =
DaveM@34 2137
DaveM@34 2138 8675
DaveM@34 2139
DaveM@34 2140 Row: 8675, pDepth = 1, loss = 0.133333
DaveM@34 2141
DaveM@34 2142 Decision tree for classification
DaveM@34 2143 1 if mfcc_dvar_0<0.103958 then node 2 elseif mfcc_dvar_0>=0.103958 then node 3 else 8592
DaveM@34 2144 2 class = 7871
DaveM@34 2145 3 class = 8592
DaveM@34 2146
DaveM@34 2147
DaveM@34 2148 row =
DaveM@34 2149
DaveM@34 2150 7889
DaveM@34 2151
DaveM@34 2152 Row: 7889, pDepth = 1, loss = 1.000000
DaveM@34 2153
DaveM@34 2154 Decision tree for classification
DaveM@34 2155 1 if mfcc_dvar_0<0.103958 then node 2 elseif mfcc_dvar_0>=0.103958 then node 3 else 8592
DaveM@34 2156 2 class = 7871
DaveM@34 2157 3 class = 8592
DaveM@34 2158
DaveM@34 2159
DaveM@34 2160 row =
DaveM@34 2161
DaveM@34 2162 8114
DaveM@34 2163
DaveM@34 2164 Row: 8114, pDepth = 1, loss = 1.000000
DaveM@34 2165
DaveM@34 2166 Decision tree for classification
DaveM@34 2167 1 if mfcc_dvar_0<0.103958 then node 2 elseif mfcc_dvar_0>=0.103958 then node 3 else 8592
DaveM@34 2168 2 class = 7871
DaveM@34 2169 3 class = 8592
DaveM@34 2170
DaveM@34 2171
DaveM@34 2172 row =
DaveM@34 2173
DaveM@34 2174 8744
DaveM@34 2175
DaveM@34 2176 Row: 8744, pDepth = 2, loss = 0.095238
DaveM@34 2177
DaveM@34 2178 Decision tree for classification
DaveM@34 2179 1 if scvalleys_var_2<0.120504 then node 2 elseif scvalleys_var_2>=0.120504 then node 3 else 8508
DaveM@34 2180 2 class = 8508
DaveM@34 2181 3 class = 8569
DaveM@34 2182
DaveM@34 2183
DaveM@34 2184 row =
DaveM@34 2185
DaveM@34 2186 8848
DaveM@34 2187
DaveM@34 2188 Row: 8848, pDepth = 5, loss = 0.179487
DaveM@34 2189
DaveM@34 2190 Decision tree for classification
DaveM@34 2191 1 if spectral_flatness_db_dmean<0.184358 then node 2 elseif spectral_flatness_db_dmean>=0.184358 then node 3 else 8777
DaveM@34 2192 2 class = 8777
DaveM@34 2193 3 class = 8801
DaveM@34 2194
DaveM@34 2195
DaveM@34 2196 row =
DaveM@34 2197
DaveM@34 2198 7522
DaveM@34 2199
DaveM@34 2200 Row: 7522, pDepth = 1, loss = 1.000000
DaveM@34 2201
DaveM@34 2202 Decision tree for classification
DaveM@34 2203 1 if spectral_flatness_db_dmean<0.184358 then node 2 elseif spectral_flatness_db_dmean>=0.184358 then node 3 else 8777
DaveM@34 2204 2 class = 8777
DaveM@34 2205 3 class = 8801
DaveM@34 2206
DaveM@34 2207
DaveM@34 2208 row =
DaveM@34 2209
DaveM@34 2210 8510
DaveM@34 2211
DaveM@34 2212 Row: 8510, pDepth = 1, loss = 1.000000
DaveM@34 2213
DaveM@34 2214 Decision tree for classification
DaveM@34 2215 1 if spectral_flatness_db_dmean<0.184358 then node 2 elseif spectral_flatness_db_dmean>=0.184358 then node 3 else 8777
DaveM@34 2216 2 class = 8777
DaveM@34 2217 3 class = 8801
DaveM@34 2218
DaveM@34 2219
DaveM@34 2220 row =
DaveM@34 2221
DaveM@34 2222 8768
DaveM@34 2223
DaveM@34 2224 Row: 8768, pDepth = 2, loss = 0.098901
DaveM@34 2225
DaveM@34 2226 Decision tree for classification
DaveM@34 2227 1 if silence_rate_30dB_dmean2<0.0486295 then node 2 elseif silence_rate_30dB_dmean2>=0.0486295 then node 3 else 8425
DaveM@34 2228 2 class = 8425
DaveM@34 2229 3 class = 8612
DaveM@34 2230
DaveM@34 2231
DaveM@34 2232 row =
DaveM@34 2233
DaveM@34 2234 8807
DaveM@34 2235
DaveM@34 2236 Row: 8807, pDepth = 2, loss = 0.078431
DaveM@34 2237
DaveM@34 2238 Decision tree for classification
DaveM@34 2239 1 if inharmonicity_mean<0.046759 then node 2 elseif inharmonicity_mean>=0.046759 then node 3 else 8724
DaveM@34 2240 2 class = 8299
DaveM@34 2241 3 class = 8724
DaveM@34 2242
DaveM@34 2243
DaveM@34 2244 row =
DaveM@34 2245
DaveM@34 2246 8460
DaveM@34 2247
DaveM@34 2248 Row: 8460, pDepth = 1, loss = 0.085714
DaveM@34 2249
DaveM@34 2250 Decision tree for classification
DaveM@34 2251 1 if spectral_contrast_mean_5<0.195317 then node 2 elseif spectral_contrast_mean_5>=0.195317 then node 3 else 8377
DaveM@34 2252 2 class = 8205
DaveM@34 2253 3 class = 8377
DaveM@34 2254
DaveM@34 2255
DaveM@34 2256 row =
DaveM@34 2257
DaveM@34 2258 8682
DaveM@34 2259
DaveM@34 2260 Row: 8682, pDepth = 1, loss = 0.018182
DaveM@34 2261
DaveM@34 2262 Decision tree for classification
DaveM@34 2263 1 if barkbands_spread_dvar<0.0142395 then node 2 elseif barkbands_spread_dvar>=0.0142395 then node 3 else 8588
DaveM@34 2264 2 class = 6448
DaveM@34 2265 3 class = 8588
DaveM@34 2266
DaveM@34 2267
DaveM@34 2268 row =
DaveM@34 2269
DaveM@34 2270 8075
DaveM@34 2271
DaveM@34 2272 Row: 8075, pDepth = 1, loss = 0.033333
DaveM@34 2273
DaveM@34 2274 Decision tree for classification
DaveM@34 2275 1 if beats_loudness_band_ratio_min_5<0.298373 then node 2 elseif beats_loudness_band_ratio_min_5>=0.298373 then node 3 else 7015
DaveM@34 2276 2 class = 7659
DaveM@34 2277 3 class = 7015
DaveM@34 2278
DaveM@34 2279
DaveM@34 2280 row =
DaveM@34 2281
DaveM@34 2282 8698
DaveM@34 2283
DaveM@34 2284 Row: 8698, pDepth = 1, loss = 1.000000
DaveM@34 2285
DaveM@34 2286 Decision tree for classification
DaveM@34 2287 1 if beats_loudness_band_ratio_min_5<0.298373 then node 2 elseif beats_loudness_band_ratio_min_5>=0.298373 then node 3 else 7015
DaveM@34 2288 2 class = 7659
DaveM@34 2289 3 class = 7015
DaveM@34 2290
DaveM@34 2291
DaveM@34 2292 row =
DaveM@34 2293
DaveM@34 2294 8711
DaveM@34 2295
DaveM@34 2296 Row: 8711, pDepth = 1, loss = 1.000000
DaveM@34 2297
DaveM@34 2298 Decision tree for classification
DaveM@34 2299 1 if beats_loudness_band_ratio_min_5<0.298373 then node 2 elseif beats_loudness_band_ratio_min_5>=0.298373 then node 3 else 7015
DaveM@34 2300 2 class = 7659
DaveM@34 2301 3 class = 7015
DaveM@34 2302
DaveM@34 2303
DaveM@34 2304 row =
DaveM@34 2305
DaveM@34 2306 8755
DaveM@34 2307
DaveM@34 2308 Row: 8755, pDepth = 2, loss = 0.197531
DaveM@34 2309
DaveM@34 2310 Decision tree for classification
DaveM@34 2311 1 if tristimulus_max_2<0.752637 then node 2 elseif tristimulus_max_2>=0.752637 then node 3 else 8658
DaveM@34 2312 2 class = 8658
DaveM@34 2313 3 if tristimulus_max_2<0.80408 then node 4 elseif tristimulus_max_2>=0.80408 then node 5 else 8658
DaveM@34 2314 4 if barkbands_dmean2_22<0.0004235 then node 6 elseif barkbands_dmean2_22>=0.0004235 then node 7 else 8382
DaveM@34 2315 5 if erb_bands_dvar2_8<0.0050135 then node 8 elseif erb_bands_dvar2_8>=0.0050135 then node 9 else 8658
DaveM@34 2316 6 class = 8382
DaveM@34 2317 7 class = 8658
DaveM@34 2318 8 class = 8658
DaveM@34 2319 9 class = 8382
DaveM@34 2320
DaveM@34 2321
DaveM@34 2322 row =
DaveM@34 2323
DaveM@34 2324 8341
DaveM@34 2325
DaveM@34 2326 Row: 8341, pDepth = 1, loss = 0.079365
DaveM@34 2327
DaveM@34 2328 Decision tree for classification
DaveM@34 2329 1 if scvalleys_var_1<0.38783 then node 2 elseif scvalleys_var_1>=0.38783 then node 3 else 8038
DaveM@34 2330 2 class = 5730
DaveM@34 2331 3 class = 8038
DaveM@34 2332
DaveM@34 2333
DaveM@34 2334 row =
DaveM@34 2335
DaveM@34 2336 8833
DaveM@34 2337
DaveM@34 2338 Row: 8833, pDepth = 5, loss = 0.142857
DaveM@34 2339
DaveM@34 2340 Decision tree for classification
DaveM@34 2341 1 if scvalleys_dmean_1<0.446352 then node 2 elseif scvalleys_dmean_1>=0.446352 then node 3 else 8758
DaveM@34 2342 2 class = 8758
DaveM@34 2343 3 class = 8740
DaveM@34 2344
DaveM@34 2345
DaveM@34 2346 row =
DaveM@34 2347
DaveM@34 2348 8632
DaveM@34 2349
DaveM@34 2350 Row: 8632, pDepth = 1, loss = 1.000000
DaveM@34 2351
DaveM@34 2352 Decision tree for classification
DaveM@34 2353 1 if scvalleys_dmean_1<0.446352 then node 2 elseif scvalleys_dmean_1>=0.446352 then node 3 else 8758
DaveM@34 2354 2 class = 8758
DaveM@34 2355 3 class = 8740
DaveM@34 2356
DaveM@34 2357
DaveM@34 2358 row =
DaveM@34 2359
DaveM@34 2360 8769
DaveM@34 2361
DaveM@34 2362 Row: 8769, pDepth = 1, loss = 1.000000
DaveM@34 2363
DaveM@34 2364 Decision tree for classification
DaveM@34 2365 1 if scvalleys_dmean_1<0.446352 then node 2 elseif scvalleys_dmean_1>=0.446352 then node 3 else 8758
DaveM@34 2366 2 class = 8758
DaveM@34 2367 3 class = 8740
DaveM@34 2368
DaveM@34 2369
DaveM@34 2370 row =
DaveM@34 2371
DaveM@34 2372 8784
DaveM@34 2373
DaveM@34 2374 Row: 8784, pDepth = 2, loss = 0.166667
DaveM@34 2375
DaveM@34 2376 Decision tree for classification
DaveM@34 2377 1 if pitch_instantaneous_confidence_median<0.454039 then node 2 elseif pitch_instantaneous_confidence_median>=0.454039 then node 3 else 8396
DaveM@34 2378 2 class = 8396
DaveM@34 2379 3 class = 8689
DaveM@34 2380
DaveM@34 2381
DaveM@34 2382 row =
DaveM@34 2383
DaveM@34 2384 8846
DaveM@34 2385
DaveM@34 2386 Row: 8846, pDepth = 2, loss = 0.048077
DaveM@34 2387
DaveM@34 2388 Decision tree for classification
DaveM@34 2389 1 if mfcc_dvar_7<0.041849 then node 2 elseif mfcc_dvar_7>=0.041849 then node 3 else 8694
DaveM@34 2390 2 class = 8694
DaveM@34 2391 3 class = 8671
DaveM@34 2392
DaveM@34 2393
DaveM@34 2394 row =
DaveM@34 2395
DaveM@34 2396 8840
DaveM@34 2397
DaveM@34 2398 Row: 8840, pDepth = 4, loss = 0.160000
DaveM@34 2399
DaveM@34 2400 Decision tree for classification
DaveM@34 2401 1 if scvalleys_min_5<0.30358 then node 2 elseif scvalleys_min_5>=0.30358 then node 3 else 8557
DaveM@34 2402 2 class = 8557
DaveM@34 2403 3 class = 8626
DaveM@34 2404
DaveM@34 2405
DaveM@34 2406 row =
DaveM@34 2407
DaveM@34 2408 8864
DaveM@34 2409
DaveM@34 2410 Row: 8864, pDepth = 2, loss = 0.132075
DaveM@34 2411
DaveM@34 2412 Decision tree for classification
DaveM@34 2413 1 if barkbands_spread_dmean<0.233654 then node 2 elseif barkbands_spread_dmean>=0.233654 then node 3 else 8813
DaveM@34 2414 2 class = 8813
DaveM@34 2415 3 class = 8566
DaveM@34 2416
DaveM@34 2417
DaveM@34 2418 row =
DaveM@34 2419
DaveM@34 2420 8838
DaveM@34 2421
DaveM@34 2422 Row: 8838, pDepth = 4, loss = 0.113636
DaveM@34 2423
DaveM@34 2424 Decision tree for classification
DaveM@34 2425 1 if second_peak_weight_min<0.310345 then node 2 elseif second_peak_weight_min>=0.310345 then node 3 else 8780
DaveM@34 2426 2 class = 8780
DaveM@34 2427 3 class = 8603
DaveM@34 2428
DaveM@34 2429
DaveM@34 2430 row =
DaveM@34 2431
DaveM@34 2432 8872
DaveM@34 2433
DaveM@34 2434 Row: 8872, pDepth = 6, loss = 0.146718
DaveM@34 2435
DaveM@34 2436 Decision tree for classification
DaveM@34 2437 1 if second_peak_spread_median<0.188947 then node 2 elseif second_peak_spread_median>=0.188947 then node 3 else 8842
DaveM@34 2438 2 if scvalleys_min_4<0.380769 then node 4 elseif scvalleys_min_4>=0.380769 then node 5 else 8789
DaveM@34 2439 3 class = 8842
DaveM@34 2440 4 class = 8789
DaveM@34 2441 5 class = 8842
DaveM@34 2442
DaveM@34 2443
DaveM@34 2444 row =
DaveM@34 2445
DaveM@34 2446 8764
DaveM@34 2447
DaveM@34 2448 Row: 8764, pDepth = 1, loss = 0.162162
DaveM@34 2449
DaveM@34 2450 Decision tree for classification
DaveM@34 2451 1 if beats_loudness_band_ratio_dmean2_4<9e-06 then node 2 elseif beats_loudness_band_ratio_dmean2_4>=9e-06 then node 3 else 8656
DaveM@34 2452 2 class = 8656
DaveM@34 2453 3 class = 8667
DaveM@34 2454
DaveM@34 2455
DaveM@34 2456 row =
DaveM@34 2457
DaveM@34 2458 8816
DaveM@34 2459
DaveM@34 2460 Row: 8816, pDepth = 2, loss = 0.092857
DaveM@34 2461
DaveM@34 2462 Decision tree for classification
DaveM@34 2463 1 if beats_loudness_band_ratio_mean_0<0.154344 then node 2 elseif beats_loudness_band_ratio_mean_0>=0.154344 then node 3 else 8746
DaveM@34 2464 2 class = 8746
DaveM@34 2465 3 class = 8699
DaveM@34 2466
DaveM@34 2467
DaveM@34 2468 row =
DaveM@34 2469
DaveM@34 2470 8738
DaveM@34 2471
DaveM@34 2472 Row: 8738, pDepth = 1, loss = 0.156863
DaveM@34 2473
DaveM@34 2474 Decision tree for classification
DaveM@34 2475 1 if mfcc_var_10<0.087524 then node 2 elseif mfcc_var_10>=0.087524 then node 3 else 8640
DaveM@34 2476 2 class = 8692
DaveM@34 2477 3 class = 8640
DaveM@34 2478
DaveM@34 2479
DaveM@34 2480 row =
DaveM@34 2481
DaveM@34 2482 8868
DaveM@34 2483
DaveM@34 2484 Row: 8868, pDepth = 4, loss = 0.130435
DaveM@34 2485
DaveM@34 2486 Decision tree for classification
DaveM@34 2487 1 if beats_loudness_band_ratio_median_0<0.00142 then node 2 elseif beats_loudness_band_ratio_median_0>=0.00142 then node 3 else 8792
DaveM@34 2488 2 class = 8792
DaveM@34 2489 3 class = 8714
DaveM@34 2490
DaveM@34 2491
DaveM@34 2492 row =
DaveM@34 2493
DaveM@34 2494 8678
DaveM@34 2495
DaveM@34 2496 Row: 8678, pDepth = 2, loss = 0.162500
DaveM@34 2497
DaveM@34 2498 Decision tree for classification
DaveM@34 2499 1 if spectral_entropy_max<0.930671 then node 2 elseif spectral_entropy_max>=0.930671 then node 3 else 8531
DaveM@34 2500 2 class = 8378
DaveM@34 2501 3 class = 8531
DaveM@34 2502
DaveM@34 2503
DaveM@34 2504 row =
DaveM@34 2505
DaveM@34 2506 8785
DaveM@34 2507
DaveM@34 2508 Row: 8785, pDepth = 2, loss = 0.077586
DaveM@34 2509
DaveM@34 2510 Decision tree for classification
DaveM@34 2511 1 if silence_rate_30dB_mean<0.974679 then node 2 elseif silence_rate_30dB_mean>=0.974679 then node 3 else 8702
DaveM@34 2512 2 class = 8604
DaveM@34 2513 3 class = 8702
DaveM@34 2514
DaveM@34 2515
DaveM@34 2516 row =
DaveM@34 2517
DaveM@34 2518 8876
DaveM@34 2519
DaveM@34 2520 Row: 8876, pDepth = 3, loss = 0.094527
DaveM@34 2521
DaveM@34 2522 Decision tree for classification
DaveM@34 2523 1 if spectral_centroid_mean<0.224613 then node 2 elseif spectral_centroid_mean>=0.224613 then node 3 else 8862
DaveM@34 2524 2 class = 8862
DaveM@34 2525 3 class = 8386
DaveM@34 2526
DaveM@34 2527
DaveM@34 2528 row =
DaveM@34 2529
DaveM@34 2530 8912
DaveM@34 2531
DaveM@34 2532 Row: 8912, pDepth = 5, loss = 0.184818
DaveM@34 2533
DaveM@34 2534 Decision tree for classification
DaveM@34 2535 1 if stopFrame<0.011142 then node 2 elseif stopFrame>=0.011142 then node 3 else 8899
DaveM@34 2536 2 class = 8737
DaveM@34 2537 3 class = 8899
DaveM@34 2538
DaveM@34 2539
DaveM@34 2540 row =
DaveM@34 2541
DaveM@34 2542 8464
DaveM@34 2543
DaveM@34 2544 Row: 8464, pDepth = 1, loss = 0.081967
DaveM@34 2545
DaveM@34 2546 Decision tree for classification
DaveM@34 2547 1 if beats_loudness_band_ratio_var_4<0.000218 then node 2 elseif beats_loudness_band_ratio_var_4>=0.000218 then node 3 else 8361
DaveM@34 2548 2 class = 8361
DaveM@34 2549 3 class = 7673
DaveM@34 2550
DaveM@34 2551
DaveM@34 2552 row =
DaveM@34 2553
DaveM@34 2554 8720
DaveM@34 2555
DaveM@34 2556 Row: 8720, pDepth = 1, loss = 1.000000
DaveM@34 2557
DaveM@34 2558 Decision tree for classification
DaveM@34 2559 1 if beats_loudness_band_ratio_var_4<0.000218 then node 2 elseif beats_loudness_band_ratio_var_4>=0.000218 then node 3 else 8361
DaveM@34 2560 2 class = 8361
DaveM@34 2561 3 class = 7673
DaveM@34 2562
DaveM@34 2563
DaveM@34 2564 row =
DaveM@34 2565
DaveM@34 2566 8790
DaveM@34 2567
DaveM@34 2568 Row: 8790, pDepth = 1, loss = 0.196970
DaveM@34 2569
DaveM@34 2570 Decision tree for classification
DaveM@34 2571 1 if barkbands_median_16<5.5e-06 then node 2 elseif barkbands_median_16>=5.5e-06 then node 3 else 8700
DaveM@34 2572 2 class = 8700
DaveM@34 2573 3 class = 8433
DaveM@34 2574
DaveM@34 2575
DaveM@34 2576 row =
DaveM@34 2577
DaveM@34 2578 8824
DaveM@34 2579
DaveM@34 2580 Row: 8824, pDepth = 1, loss = 0.054795
DaveM@34 2581
DaveM@34 2582 Decision tree for classification
DaveM@34 2583 1 if mfcc_median_9<0.605751 then node 2 elseif mfcc_median_9>=0.605751 then node 3 else 8666
DaveM@34 2584 2 class = 8666
DaveM@34 2585 3 class = 8610
DaveM@34 2586
DaveM@34 2587
DaveM@34 2588 row =
DaveM@34 2589
DaveM@34 2590 8835
DaveM@34 2591
DaveM@34 2592 Row: 8835, pDepth = 1, loss = 1.000000
DaveM@34 2593
DaveM@34 2594 Decision tree for classification
DaveM@34 2595 1 if mfcc_median_9<0.605751 then node 2 elseif mfcc_median_9>=0.605751 then node 3 else 8666
DaveM@34 2596 2 class = 8666
DaveM@34 2597 3 class = 8610
DaveM@34 2598
DaveM@34 2599
DaveM@34 2600 row =
DaveM@34 2601
DaveM@34 2602 8871
DaveM@34 2603
DaveM@34 2604 Row: 8871, pDepth = 2, loss = 0.065789
DaveM@34 2605
DaveM@34 2606 Decision tree for classification
DaveM@34 2607 1 if inharmonicity_mean<0.193569 then node 2 elseif inharmonicity_mean>=0.193569 then node 3 else 8796
DaveM@34 2608 2 class = 8796
DaveM@34 2609 3 class = 8709
DaveM@34 2610
DaveM@34 2611
DaveM@34 2612 row =
DaveM@34 2613
DaveM@34 2614 8910
DaveM@34 2615
DaveM@34 2616 Row: 8910, pDepth = 3, loss = 0.059896
DaveM@34 2617
DaveM@34 2618 Decision tree for classification
DaveM@34 2619 1 if spectral_decrease_median<0.899888 then node 2 elseif spectral_decrease_median>=0.899888 then node 3 else 8878
DaveM@34 2620 2 class = 8810
DaveM@34 2621 3 class = 8878
DaveM@34 2622
DaveM@34 2623
DaveM@34 2624 row =
DaveM@34 2625
DaveM@34 2626 8946
DaveM@34 2627
DaveM@34 2628 Row: 8946, pDepth = 1, loss = 1.000000
DaveM@34 2629
DaveM@34 2630 Decision tree for classification
DaveM@34 2631 1 if spectral_decrease_median<0.899888 then node 2 elseif spectral_decrease_median>=0.899888 then node 3 else 8878
DaveM@34 2632 2 class = 8810
DaveM@34 2633 3 class = 8878
DaveM@34 2634
DaveM@34 2635
DaveM@34 2636 row =
DaveM@34 2637
DaveM@34 2638 8819
DaveM@34 2639
DaveM@34 2640 Row: 8819, pDepth = 3, loss = 0.118110
DaveM@34 2641
DaveM@34 2642 Decision tree for classification
DaveM@34 2643 1 if spectral_centroid_median<0.16468 then node 2 elseif spectral_centroid_median>=0.16468 then node 3 else 8648
DaveM@34 2644 2 class = 8590
DaveM@34 2645 3 class = 8648
DaveM@34 2646
DaveM@34 2647
DaveM@34 2648 row =
DaveM@34 2649
DaveM@34 2650 8837
DaveM@34 2651
DaveM@34 2652 Row: 8837, pDepth = 1, loss = 1.000000
DaveM@34 2653
DaveM@34 2654 Decision tree for classification
DaveM@34 2655 1 if spectral_centroid_median<0.16468 then node 2 elseif spectral_centroid_median>=0.16468 then node 3 else 8648
DaveM@34 2656 2 class = 8590
DaveM@34 2657 3 class = 8648
DaveM@34 2658
DaveM@34 2659
DaveM@34 2660 row =
DaveM@34 2661
DaveM@34 2662 8843
DaveM@34 2663
DaveM@34 2664 Row: 8843, pDepth = 1, loss = 0.065217
DaveM@34 2665
DaveM@34 2666 Decision tree for classification
DaveM@34 2667 1 if beats_loudness_band_ratio_max_5<0.332682 then node 2 elseif beats_loudness_band_ratio_max_5>=0.332682 then node 3 else 8673
DaveM@34 2668 2 class = 8713
DaveM@34 2669 3 class = 8673
DaveM@34 2670
DaveM@34 2671
DaveM@34 2672 row =
DaveM@34 2673
DaveM@34 2674 8891
DaveM@34 2675
DaveM@34 2676 Row: 8891, pDepth = 2, loss = 0.076471
DaveM@34 2677
DaveM@34 2678 Decision tree for classification
DaveM@34 2679 1 if spectral_entropy_mean<0.712979 then node 2 elseif spectral_entropy_mean>=0.712979 then node 3 else 8851
DaveM@34 2680 2 class = 8742
DaveM@34 2681 3 class = 8851
DaveM@34 2682
DaveM@34 2683
DaveM@34 2684 row =
DaveM@34 2685
DaveM@34 2686 8852
DaveM@34 2687
DaveM@34 2688 Row: 8852, pDepth = 1, loss = 0.053763
DaveM@34 2689
DaveM@34 2690 Decision tree for classification
DaveM@34 2691 1 if scvalleys_mean_2<0.799317 then node 2 elseif scvalleys_mean_2>=0.799317 then node 3 else 8618
DaveM@34 2692 2 class = 8458
DaveM@34 2693 3 class = 8618
DaveM@34 2694
DaveM@34 2695
DaveM@34 2696 row =
DaveM@34 2697
DaveM@34 2698 8873
DaveM@34 2699
DaveM@34 2700 Row: 8873, pDepth = 5, loss = 0.188679
DaveM@34 2701
DaveM@34 2702 Decision tree for classification
DaveM@34 2703 1 if gfcc_max_0<0.8201 then node 2 elseif gfcc_max_0>=0.8201 then node 3 else 8830
DaveM@34 2704 2 class = 8817
DaveM@34 2705 3 class = 8830
DaveM@34 2706
DaveM@34 2707
DaveM@34 2708 row =
DaveM@34 2709
DaveM@34 2710 8857
DaveM@34 2711
DaveM@34 2712 Row: 8857, pDepth = 3, loss = 0.078740
DaveM@34 2713
DaveM@34 2714 Decision tree for classification
DaveM@34 2715 1 if silence_rate_30dB_dvar<0.0081965 then node 2 elseif silence_rate_30dB_dvar>=0.0081965 then node 3 else 8800
DaveM@34 2716 2 class = 8521
DaveM@34 2717 3 class = 8800
DaveM@34 2718
DaveM@34 2719
DaveM@34 2720 row =
DaveM@34 2721
DaveM@34 2722 8921
DaveM@34 2723
DaveM@34 2724 Row: 8921, pDepth = 9, loss = 0.196481
DaveM@34 2725
DaveM@34 2726 Decision tree for classification
DaveM@34 2727 1 if beats_loudness_band_ratio_min_4<1.5e-06 then node 2 elseif beats_loudness_band_ratio_min_4>=1.5e-06 then node 3 else 8889
DaveM@34 2728 2 class = 8889
DaveM@34 2729 3 class = 8845
DaveM@34 2730
DaveM@34 2731
DaveM@34 2732 row =
DaveM@34 2733
DaveM@34 2734 8156
DaveM@34 2735
DaveM@34 2736 Row: 8156, pDepth = 1, loss = 1.000000
DaveM@34 2737
DaveM@34 2738 Decision tree for classification
DaveM@34 2739 1 if beats_loudness_band_ratio_min_4<1.5e-06 then node 2 elseif beats_loudness_band_ratio_min_4>=1.5e-06 then node 3 else 8889
DaveM@34 2740 2 class = 8889
DaveM@34 2741 3 class = 8845
DaveM@34 2742
DaveM@34 2743
DaveM@34 2744 row =
DaveM@34 2745
DaveM@34 2746 8301
DaveM@34 2747
DaveM@34 2748 Row: 8301, pDepth = 1, loss = 1.000000
DaveM@34 2749
DaveM@34 2750 Decision tree for classification
DaveM@34 2751 1 if beats_loudness_band_ratio_min_4<1.5e-06 then node 2 elseif beats_loudness_band_ratio_min_4>=1.5e-06 then node 3 else 8889
DaveM@34 2752 2 class = 8889
DaveM@34 2753 3 class = 8845
DaveM@34 2754
DaveM@34 2755
DaveM@34 2756 row =
DaveM@34 2757
DaveM@34 2758 8063
DaveM@34 2759
DaveM@34 2760 Row: 8063, pDepth = 1, loss = 1.000000
DaveM@34 2761
DaveM@34 2762 Decision tree for classification
DaveM@34 2763 1 if beats_loudness_band_ratio_min_4<1.5e-06 then node 2 elseif beats_loudness_band_ratio_min_4>=1.5e-06 then node 3 else 8889
DaveM@34 2764 2 class = 8889
DaveM@34 2765 3 class = 8845
DaveM@34 2766
DaveM@34 2767
DaveM@34 2768 row =
DaveM@34 2769
DaveM@34 2770 8327
DaveM@34 2771
DaveM@34 2772 Row: 8327, pDepth = 1, loss = 0.055556
DaveM@34 2773
DaveM@34 2774 Decision tree for classification
DaveM@34 2775 1 if scvalleys_min_3<0.3427 then node 2 elseif scvalleys_min_3>=0.3427 then node 3 else 7995
DaveM@34 2776 2 class = 7995
DaveM@34 2777 3 class = 6675
DaveM@34 2778
DaveM@34 2779
DaveM@34 2780 row =
DaveM@34 2781
DaveM@34 2782 8869
DaveM@34 2783
DaveM@34 2784 Row: 8869, pDepth = 4, loss = 0.113990
DaveM@34 2785
DaveM@34 2786 Decision tree for classification
DaveM@34 2787 1 if spectral_entropy_min<0.606841 then node 2 elseif spectral_entropy_min>=0.606841 then node 3 else 8820
DaveM@34 2788 2 class = 8820
DaveM@34 2789 3 class = 8669
DaveM@34 2790
DaveM@34 2791
DaveM@34 2792 row =
DaveM@34 2793
DaveM@34 2794 8883
DaveM@34 2795
DaveM@34 2796 Row: 8883, pDepth = 5, loss = 0.197917
DaveM@34 2797
DaveM@34 2798 Decision tree for classification
DaveM@34 2799 1 if scvalleys_min_2<0.498872 then node 2 elseif scvalleys_min_2>=0.498872 then node 3 else 8795
DaveM@34 2800 2 class = 8795
DaveM@34 2801 3 class = 8859
DaveM@34 2802
DaveM@34 2803
DaveM@34 2804 row =
DaveM@34 2805
DaveM@34 2806 8894
DaveM@34 2807
DaveM@34 2808 Row: 8894, pDepth = 2, loss = 0.064286
DaveM@34 2809
DaveM@34 2810 Decision tree for classification
DaveM@34 2811 1 if inharmonicity_var<0.003146 then node 2 elseif inharmonicity_var>=0.003146 then node 3 else 8844
DaveM@34 2812 2 class = 8834
DaveM@34 2813 3 class = 8844
DaveM@34 2814
DaveM@34 2815
DaveM@34 2816 row =
DaveM@34 2817
DaveM@34 2818 8908
DaveM@34 2819
DaveM@34 2820 Row: 8908, pDepth = 3, loss = 0.161765
DaveM@34 2821
DaveM@34 2822 Decision tree for classification
DaveM@34 2823 1 if gfcc_dmean_0<0.024685 then node 2 elseif gfcc_dmean_0>=0.024685 then node 3 else 8853
DaveM@34 2824 2 class = 8849
DaveM@34 2825 3 class = 8853
DaveM@34 2826
DaveM@34 2827
DaveM@34 2828 row =
DaveM@34 2829
DaveM@34 2830 7240
DaveM@34 2831
DaveM@34 2832 Row: 7240, pDepth = 0, loss = 1.000000
DaveM@34 2833
DaveM@34 2834 Decision tree for classification
DaveM@34 2835 1 if gfcc_dmean_0<0.024685 then node 2 elseif gfcc_dmean_0>=0.024685 then node 3 else 8853
DaveM@34 2836 2 class = 8849
DaveM@34 2837 3 class = 8853
DaveM@34 2838
DaveM@34 2839
DaveM@34 2840 row =
DaveM@34 2841
DaveM@34 2842 8053
DaveM@34 2843
DaveM@34 2844 Row: 8053, pDepth = 1, loss = 0.045455
DaveM@34 2845
DaveM@34 2846 Decision tree for classification
DaveM@34 2847 1 if beats_loudness_band_ratio_dmean2_5<0.353328 then node 2 elseif beats_loudness_band_ratio_dmean2_5>=0.353328 then node 3 else 7824
DaveM@34 2848 2 class = 7824
DaveM@34 2849 3 class = 6868
DaveM@34 2850
DaveM@34 2851
DaveM@34 2852 row =
DaveM@34 2853
DaveM@34 2854 7891
DaveM@34 2855
DaveM@34 2856 Row: 7891, pDepth = 0, loss = 1.000000
DaveM@34 2857
DaveM@34 2858 Decision tree for classification
DaveM@34 2859 1 if beats_loudness_band_ratio_dmean2_5<0.353328 then node 2 elseif beats_loudness_band_ratio_dmean2_5>=0.353328 then node 3 else 7824
DaveM@34 2860 2 class = 7824
DaveM@34 2861 3 class = 6868
DaveM@34 2862
DaveM@34 2863
DaveM@34 2864 row =
DaveM@34 2865
DaveM@34 2866 8602
DaveM@34 2867
DaveM@34 2868 Row: 8602, pDepth = 1, loss = 0.081633
DaveM@34 2869
DaveM@34 2870 Decision tree for classification
DaveM@34 2871 1 if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
DaveM@34 2872 2 class = 8539
DaveM@34 2873 3 class = 8172
DaveM@34 2874
DaveM@34 2875
DaveM@34 2876 row =
DaveM@34 2877
DaveM@34 2878 1755
DaveM@34 2879
DaveM@34 2880 Row: 1755, pDepth = 0, loss = 1.000000
DaveM@34 2881
DaveM@34 2882 Decision tree for classification
DaveM@34 2883 1 if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
DaveM@34 2884 2 class = 8539
DaveM@34 2885 3 class = 8172
DaveM@34 2886
DaveM@34 2887
DaveM@34 2888 row =
DaveM@34 2889
DaveM@34 2890 7828
DaveM@34 2891
DaveM@34 2892 Row: 7828, pDepth = 0, loss = 1.000000
DaveM@34 2893
DaveM@34 2894 Decision tree for classification
DaveM@34 2895 1 if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
DaveM@34 2896 2 class = 8539
DaveM@34 2897 3 class = 8172
DaveM@34 2898
DaveM@34 2899
DaveM@34 2900 row =
DaveM@34 2901
DaveM@34 2902 8688
DaveM@34 2903
DaveM@34 2904 Row: 8688, pDepth = 1, loss = 1.000000
DaveM@34 2905
DaveM@34 2906 Decision tree for classification
DaveM@34 2907 1 if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
DaveM@34 2908 2 class = 8539
DaveM@34 2909 3 class = 8172
DaveM@34 2910
DaveM@34 2911
DaveM@34 2912 row =
DaveM@34 2913
DaveM@34 2914 8808
DaveM@34 2915
DaveM@34 2916 Row: 8808, pDepth = 1, loss = 1.000000
DaveM@34 2917
DaveM@34 2918 Decision tree for classification
DaveM@34 2919 1 if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
DaveM@34 2920 2 class = 8539
DaveM@34 2921 3 class = 8172
DaveM@34 2922
DaveM@34 2923
DaveM@34 2924 row =
DaveM@34 2925
DaveM@34 2926 6569
DaveM@34 2927
DaveM@34 2928 Row: 6569, pDepth = 1, loss = 1.000000
DaveM@34 2929
DaveM@34 2930 Decision tree for classification
DaveM@34 2931 1 if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
DaveM@34 2932 2 class = 8539
DaveM@34 2933 3 class = 8172
DaveM@34 2934
DaveM@34 2935
DaveM@34 2936 row =
DaveM@34 2937
DaveM@34 2938 6932
DaveM@34 2939
DaveM@34 2940 Row: 6932, pDepth = 0, loss = 1.000000
DaveM@34 2941
DaveM@34 2942 Decision tree for classification
DaveM@34 2943 1 if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
DaveM@34 2944 2 class = 8539
DaveM@34 2945 3 class = 8172
DaveM@34 2946
DaveM@34 2947
DaveM@34 2948 row =
DaveM@34 2949
DaveM@34 2950 8896
DaveM@34 2951
DaveM@34 2952 Row: 8896, pDepth = 1, loss = 0.017544
DaveM@34 2953
DaveM@34 2954 Decision tree for classification
DaveM@34 2955 1 if spectral_rms_mean<0.162661 then node 2 elseif spectral_rms_mean>=0.162661 then node 3 else 8806
DaveM@34 2956 2 class = 8806
DaveM@34 2957 3 class = 8687
DaveM@34 2958
DaveM@34 2959
DaveM@34 2960 row =
DaveM@34 2961
DaveM@34 2962 8927
DaveM@34 2963
DaveM@34 2964 Row: 8927, pDepth = 1, loss = 1.000000
DaveM@34 2965
DaveM@34 2966 Decision tree for classification
DaveM@34 2967 1 if spectral_rms_mean<0.162661 then node 2 elseif spectral_rms_mean>=0.162661 then node 3 else 8806
DaveM@34 2968 2 class = 8806
DaveM@34 2969 3 class = 8687
DaveM@34 2970
DaveM@34 2971
DaveM@34 2972 row =
DaveM@34 2973
DaveM@34 2974 8767
DaveM@34 2975
DaveM@34 2976 Row: 8767, pDepth = 1, loss = 0.105263
DaveM@34 2977
DaveM@34 2978 Decision tree for classification
DaveM@34 2979 1 if second_peak_bpm_max<0.666667 then node 2 elseif second_peak_bpm_max>=0.666667 then node 3 else 8736
DaveM@34 2980 2 class = 8736
DaveM@34 2981 3 class = 7749
DaveM@34 2982
DaveM@34 2983
DaveM@34 2984 row =
DaveM@34 2985
DaveM@34 2986 8882
DaveM@34 2987
DaveM@34 2988 Row: 8882, pDepth = 4, loss = 0.120219
DaveM@34 2989
DaveM@34 2990 Decision tree for classification
DaveM@34 2991 1 if scvalleys_min_3<0.370889 then node 2 elseif scvalleys_min_3>=0.370889 then node 3 else 8874
DaveM@34 2992 2 class = 8874
DaveM@34 2993 3 class = 8805
DaveM@34 2994
DaveM@34 2995
DaveM@34 2996 row =
DaveM@34 2997
DaveM@34 2998 8781
DaveM@34 2999
DaveM@34 3000 Row: 8781, pDepth = 4, loss = 0.179245
DaveM@34 3001
DaveM@34 3002 Decision tree for classification
DaveM@34 3003 1 if beats_loudness_band_ratio_median_1<0.000963 then node 2 elseif beats_loudness_band_ratio_median_1>=0.000963 then node 3 else 8654
DaveM@34 3004 2 class = 8654
DaveM@34 3005 3 class = 8706
DaveM@34 3006
DaveM@34 3007
DaveM@34 3008 row =
DaveM@34 3009
DaveM@34 3010 8914
DaveM@34 3011
DaveM@34 3012 Row: 8914, pDepth = 6, loss = 0.141791
DaveM@34 3013
DaveM@34 3014 Decision tree for classification
DaveM@34 3015 1 if pitch_instantaneous_confidence_mean<0.731062 then node 2 elseif pitch_instantaneous_confidence_mean>=0.731062 then node 3 else 8887
DaveM@34 3016 2 class = 8887
DaveM@34 3017 3 class = 8822
DaveM@34 3018
DaveM@34 3019
DaveM@34 3020 row =
DaveM@34 3021
DaveM@34 3022 8823
DaveM@34 3023
DaveM@34 3024 Row: 8823, pDepth = 2, loss = 0.147059
DaveM@34 3025
DaveM@34 3026 Decision tree for classification
DaveM@34 3027 1 if barkbands_median_9<2.15e-05 then node 2 elseif barkbands_median_9>=2.15e-05 then node 3 else 8793
DaveM@34 3028 2 class = 8593
DaveM@34 3029 3 class = 8793
DaveM@34 3030
DaveM@34 3031
DaveM@34 3032 row =
DaveM@34 3033
DaveM@34 3034 8916
DaveM@34 3035
DaveM@34 3036 Row: 8916, pDepth = 3, loss = 0.076305
DaveM@34 3037
DaveM@34 3038 Decision tree for classification
DaveM@34 3039 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3040 2 class = 8907
DaveM@34 3041 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3042 4 class = 8867
DaveM@34 3043 5 class = 8907
DaveM@34 3044
DaveM@34 3045
DaveM@34 3046 row =
DaveM@34 3047
DaveM@34 3048 6869
DaveM@34 3049
DaveM@34 3050 Row: 6869, pDepth = 0, loss = 1.000000
DaveM@34 3051
DaveM@34 3052 Decision tree for classification
DaveM@34 3053 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3054 2 class = 8907
DaveM@34 3055 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3056 4 class = 8867
DaveM@34 3057 5 class = 8907
DaveM@34 3058
DaveM@34 3059
DaveM@34 3060 row =
DaveM@34 3061
DaveM@34 3062 7664
DaveM@34 3063
DaveM@34 3064 Row: 7664, pDepth = 0, loss = 1.000000
DaveM@34 3065
DaveM@34 3066 Decision tree for classification
DaveM@34 3067 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3068 2 class = 8907
DaveM@34 3069 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3070 4 class = 8867
DaveM@34 3071 5 class = 8907
DaveM@34 3072
DaveM@34 3073
DaveM@34 3074 row =
DaveM@34 3075
DaveM@34 3076 8071
DaveM@34 3077
DaveM@34 3078 Row: 8071, pDepth = 0, loss = 1.000000
DaveM@34 3079
DaveM@34 3080 Decision tree for classification
DaveM@34 3081 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3082 2 class = 8907
DaveM@34 3083 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3084 4 class = 8867
DaveM@34 3085 5 class = 8907
DaveM@34 3086
DaveM@34 3087
DaveM@34 3088 row =
DaveM@34 3089
DaveM@34 3090 8111
DaveM@34 3091
DaveM@34 3092 Row: 8111, pDepth = 0, loss = 1.000000
DaveM@34 3093
DaveM@34 3094 Decision tree for classification
DaveM@34 3095 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3096 2 class = 8907
DaveM@34 3097 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3098 4 class = 8867
DaveM@34 3099 5 class = 8907
DaveM@34 3100
DaveM@34 3101
DaveM@34 3102 row =
DaveM@34 3103
DaveM@34 3104 7518
DaveM@34 3105
DaveM@34 3106 Row: 7518, pDepth = 0, loss = 1.000000
DaveM@34 3107
DaveM@34 3108 Decision tree for classification
DaveM@34 3109 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3110 2 class = 8907
DaveM@34 3111 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3112 4 class = 8867
DaveM@34 3113 5 class = 8907
DaveM@34 3114
DaveM@34 3115
DaveM@34 3116 row =
DaveM@34 3117
DaveM@34 3118 7834
DaveM@34 3119
DaveM@34 3120 Row: 7834, pDepth = 0, loss = 1.000000
DaveM@34 3121
DaveM@34 3122 Decision tree for classification
DaveM@34 3123 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3124 2 class = 8907
DaveM@34 3125 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3126 4 class = 8867
DaveM@34 3127 5 class = 8907
DaveM@34 3128
DaveM@34 3129
DaveM@34 3130 row =
DaveM@34 3131
DaveM@34 3132 6008
DaveM@34 3133
DaveM@34 3134 Row: 6008, pDepth = 0, loss = 1.000000
DaveM@34 3135
DaveM@34 3136 Decision tree for classification
DaveM@34 3137 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3138 2 class = 8907
DaveM@34 3139 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3140 4 class = 8867
DaveM@34 3141 5 class = 8907
DaveM@34 3142
DaveM@34 3143
DaveM@34 3144 row =
DaveM@34 3145
DaveM@34 3146 6260
DaveM@34 3147
DaveM@34 3148 Row: 6260, pDepth = 1, loss = 1.000000
DaveM@34 3149
DaveM@34 3150 Decision tree for classification
DaveM@34 3151 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3152 2 class = 8907
DaveM@34 3153 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3154 4 class = 8867
DaveM@34 3155 5 class = 8907
DaveM@34 3156
DaveM@34 3157
DaveM@34 3158 row =
DaveM@34 3159
DaveM@34 3160 7257
DaveM@34 3161
DaveM@34 3162 Row: 7257, pDepth = 0, loss = 1.000000
DaveM@34 3163
DaveM@34 3164 Decision tree for classification
DaveM@34 3165 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3166 2 class = 8907
DaveM@34 3167 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3168 4 class = 8867
DaveM@34 3169 5 class = 8907
DaveM@34 3170
DaveM@34 3171
DaveM@34 3172 row =
DaveM@34 3173
DaveM@34 3174 8276
DaveM@34 3175
DaveM@34 3176 Row: 8276, pDepth = 1, loss = 1.000000
DaveM@34 3177
DaveM@34 3178 Decision tree for classification
DaveM@34 3179 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3180 2 class = 8907
DaveM@34 3181 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3182 4 class = 8867
DaveM@34 3183 5 class = 8907
DaveM@34 3184
DaveM@34 3185
DaveM@34 3186 row =
DaveM@34 3187
DaveM@34 3188 8492
DaveM@34 3189
DaveM@34 3190 Row: 8492, pDepth = 1, loss = 1.000000
DaveM@34 3191
DaveM@34 3192 Decision tree for classification
DaveM@34 3193 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3194 2 class = 8907
DaveM@34 3195 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3196 4 class = 8867
DaveM@34 3197 5 class = 8907
DaveM@34 3198
DaveM@34 3199
DaveM@34 3200 row =
DaveM@34 3201
DaveM@34 3202 8412
DaveM@34 3203
DaveM@34 3204 Row: 8412, pDepth = 1, loss = 1.000000
DaveM@34 3205
DaveM@34 3206 Decision tree for classification
DaveM@34 3207 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3208 2 class = 8907
DaveM@34 3209 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3210 4 class = 8867
DaveM@34 3211 5 class = 8907
DaveM@34 3212
DaveM@34 3213
DaveM@34 3214 row =
DaveM@34 3215
DaveM@34 3216 8517
DaveM@34 3217
DaveM@34 3218 Row: 8517, pDepth = 0, loss = 1.000000
DaveM@34 3219
DaveM@34 3220 Decision tree for classification
DaveM@34 3221 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3222 2 class = 8907
DaveM@34 3223 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3224 4 class = 8867
DaveM@34 3225 5 class = 8907
DaveM@34 3226
DaveM@34 3227
DaveM@34 3228 row =
DaveM@34 3229
DaveM@34 3230 6450
DaveM@34 3231
DaveM@34 3232 Row: 6450, pDepth = 0, loss = 1.000000
DaveM@34 3233
DaveM@34 3234 Decision tree for classification
DaveM@34 3235 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3236 2 class = 8907
DaveM@34 3237 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3238 4 class = 8867
DaveM@34 3239 5 class = 8907
DaveM@34 3240
DaveM@34 3241
DaveM@34 3242 row =
DaveM@34 3243
DaveM@34 3244 6973
DaveM@34 3245
DaveM@34 3246 Row: 6973, pDepth = 0, loss = 1.000000
DaveM@34 3247
DaveM@34 3248 Decision tree for classification
DaveM@34 3249 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3250 2 class = 8907
DaveM@34 3251 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3252 4 class = 8867
DaveM@34 3253 5 class = 8907
DaveM@34 3254
DaveM@34 3255
DaveM@34 3256 row =
DaveM@34 3257
DaveM@34 3258 7691
DaveM@34 3259
DaveM@34 3260 Row: 7691, pDepth = 0, loss = 1.000000
DaveM@34 3261
DaveM@34 3262 Decision tree for classification
DaveM@34 3263 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3264 2 class = 8907
DaveM@34 3265 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3266 4 class = 8867
DaveM@34 3267 5 class = 8907
DaveM@34 3268
DaveM@34 3269
DaveM@34 3270 row =
DaveM@34 3271
DaveM@34 3272 7528
DaveM@34 3273
DaveM@34 3274 Row: 7528, pDepth = 1, loss = 1.000000
DaveM@34 3275
DaveM@34 3276 Decision tree for classification
DaveM@34 3277 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3278 2 class = 8907
DaveM@34 3279 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3280 4 class = 8867
DaveM@34 3281 5 class = 8907
DaveM@34 3282
DaveM@34 3283
DaveM@34 3284 row =
DaveM@34 3285
DaveM@34 3286 8235
DaveM@34 3287
DaveM@34 3288 Row: 8235, pDepth = 1, loss = 1.000000
DaveM@34 3289
DaveM@34 3290 Decision tree for classification
DaveM@34 3291 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3292 2 class = 8907
DaveM@34 3293 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3294 4 class = 8867
DaveM@34 3295 5 class = 8907
DaveM@34 3296
DaveM@34 3297
DaveM@34 3298 row =
DaveM@34 3299
DaveM@34 3300 7471
DaveM@34 3301
DaveM@34 3302 Row: 7471, pDepth = 0, loss = 1.000000
DaveM@34 3303
DaveM@34 3304 Decision tree for classification
DaveM@34 3305 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3306 2 class = 8907
DaveM@34 3307 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3308 4 class = 8867
DaveM@34 3309 5 class = 8907
DaveM@34 3310
DaveM@34 3311
DaveM@34 3312 row =
DaveM@34 3313
DaveM@34 3314 7963
DaveM@34 3315
DaveM@34 3316 Row: 7963, pDepth = 1, loss = 1.000000
DaveM@34 3317
DaveM@34 3318 Decision tree for classification
DaveM@34 3319 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3320 2 class = 8907
DaveM@34 3321 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3322 4 class = 8867
DaveM@34 3323 5 class = 8907
DaveM@34 3324
DaveM@34 3325
DaveM@34 3326 row =
DaveM@34 3327
DaveM@34 3328 8387
DaveM@34 3329
DaveM@34 3330 Row: 8387, pDepth = 1, loss = 1.000000
DaveM@34 3331
DaveM@34 3332 Decision tree for classification
DaveM@34 3333 1 if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
DaveM@34 3334 2 class = 8907
DaveM@34 3335 3 if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
DaveM@34 3336 4 class = 8867
DaveM@34 3337 5 class = 8907
DaveM@34 3338
DaveM@34 3339
DaveM@34 3340 row =
DaveM@34 3341
DaveM@34 3342 8563
DaveM@34 3343
DaveM@34 3344 Row: 8563, pDepth = 1, loss = 0.181818
DaveM@34 3345
DaveM@34 3346 Decision tree for classification
DaveM@34 3347 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3348 2 class = 7936
DaveM@34 3349 3 class = 8411
DaveM@34 3350
DaveM@34 3351
DaveM@34 3352 row =
DaveM@34 3353
DaveM@34 3354 7277
DaveM@34 3355
DaveM@34 3356 Row: 7277, pDepth = 0, loss = 1.000000
DaveM@34 3357
DaveM@34 3358 Decision tree for classification
DaveM@34 3359 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3360 2 class = 7936
DaveM@34 3361 3 class = 8411
DaveM@34 3362
DaveM@34 3363
DaveM@34 3364 row =
DaveM@34 3365
DaveM@34 3366 7408
DaveM@34 3367
DaveM@34 3368 Row: 7408, pDepth = 1, loss = 1.000000
DaveM@34 3369
DaveM@34 3370 Decision tree for classification
DaveM@34 3371 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3372 2 class = 7936
DaveM@34 3373 3 class = 8411
DaveM@34 3374
DaveM@34 3375
DaveM@34 3376 row =
DaveM@34 3377
DaveM@34 3378 8310
DaveM@34 3379
DaveM@34 3380 Row: 8310, pDepth = 0, loss = 1.000000
DaveM@34 3381
DaveM@34 3382 Decision tree for classification
DaveM@34 3383 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3384 2 class = 7936
DaveM@34 3385 3 class = 8411
DaveM@34 3386
DaveM@34 3387
DaveM@34 3388 row =
DaveM@34 3389
DaveM@34 3390 8348
DaveM@34 3391
DaveM@34 3392 Row: 8348, pDepth = 1, loss = 1.000000
DaveM@34 3393
DaveM@34 3394 Decision tree for classification
DaveM@34 3395 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3396 2 class = 7936
DaveM@34 3397 3 class = 8411
DaveM@34 3398
DaveM@34 3399
DaveM@34 3400 row =
DaveM@34 3401
DaveM@34 3402 1788
DaveM@34 3403
DaveM@34 3404 Row: 1788, pDepth = 0, loss = 1.000000
DaveM@34 3405
DaveM@34 3406 Decision tree for classification
DaveM@34 3407 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3408 2 class = 7936
DaveM@34 3409 3 class = 8411
DaveM@34 3410
DaveM@34 3411
DaveM@34 3412 row =
DaveM@34 3413
DaveM@34 3414 928
DaveM@34 3415
DaveM@34 3416 Row: 928, pDepth = 1, loss = 1.000000
DaveM@34 3417
DaveM@34 3418 Decision tree for classification
DaveM@34 3419 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3420 2 class = 7936
DaveM@34 3421 3 class = 8411
DaveM@34 3422
DaveM@34 3423
DaveM@34 3424 row =
DaveM@34 3425
DaveM@34 3426 2329
DaveM@34 3427
DaveM@34 3428 Row: 2329, pDepth = 0, loss = 1.000000
DaveM@34 3429
DaveM@34 3430 Decision tree for classification
DaveM@34 3431 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3432 2 class = 7936
DaveM@34 3433 3 class = 8411
DaveM@34 3434
DaveM@34 3435
DaveM@34 3436 row =
DaveM@34 3437
DaveM@34 3438 7614
DaveM@34 3439
DaveM@34 3440 Row: 7614, pDepth = 0, loss = 1.000000
DaveM@34 3441
DaveM@34 3442 Decision tree for classification
DaveM@34 3443 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3444 2 class = 7936
DaveM@34 3445 3 class = 8411
DaveM@34 3446
DaveM@34 3447
DaveM@34 3448 row =
DaveM@34 3449
DaveM@34 3450 8046
DaveM@34 3451
DaveM@34 3452 Row: 8046, pDepth = 1, loss = 1.000000
DaveM@34 3453
DaveM@34 3454 Decision tree for classification
DaveM@34 3455 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3456 2 class = 7936
DaveM@34 3457 3 class = 8411
DaveM@34 3458
DaveM@34 3459
DaveM@34 3460 row =
DaveM@34 3461
DaveM@34 3462 8491
DaveM@34 3463
DaveM@34 3464 Row: 8491, pDepth = 0, loss = 1.000000
DaveM@34 3465
DaveM@34 3466 Decision tree for classification
DaveM@34 3467 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3468 2 class = 7936
DaveM@34 3469 3 class = 8411
DaveM@34 3470
DaveM@34 3471
DaveM@34 3472 row =
DaveM@34 3473
DaveM@34 3474 8614
DaveM@34 3475
DaveM@34 3476 Row: 8614, pDepth = 1, loss = 1.000000
DaveM@34 3477
DaveM@34 3478 Decision tree for classification
DaveM@34 3479 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3480 2 class = 7936
DaveM@34 3481 3 class = 8411
DaveM@34 3482
DaveM@34 3483
DaveM@34 3484 row =
DaveM@34 3485
DaveM@34 3486 8645
DaveM@34 3487
DaveM@34 3488 Row: 8645, pDepth = 1, loss = 1.000000
DaveM@34 3489
DaveM@34 3490 Decision tree for classification
DaveM@34 3491 1 if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
DaveM@34 3492 2 class = 7936
DaveM@34 3493 3 class = 8411
DaveM@34 3494
DaveM@34 3495
DaveM@34 3496 row =
DaveM@34 3497
DaveM@34 3498 8719
DaveM@34 3499
DaveM@34 3500 Row: 8719, pDepth = 2, loss = 0.178571
DaveM@34 3501
DaveM@34 3502 Decision tree for classification
DaveM@34 3503 1 if spectral_rolloff_median<0.122574 then node 2 elseif spectral_rolloff_median>=0.122574 then node 3 else 8571
DaveM@34 3504 2 class = 8571
DaveM@34 3505 3 class = 8639
DaveM@34 3506
DaveM@34 3507
DaveM@34 3508 row =
DaveM@34 3509
DaveM@34 3510 8791
DaveM@34 3511
DaveM@34 3512 Row: 8791, pDepth = 1, loss = 1.000000
DaveM@34 3513
DaveM@34 3514 Decision tree for classification
DaveM@34 3515 1 if spectral_rolloff_median<0.122574 then node 2 elseif spectral_rolloff_median>=0.122574 then node 3 else 8571
DaveM@34 3516 2 class = 8571
DaveM@34 3517 3 class = 8639
DaveM@34 3518
DaveM@34 3519
DaveM@34 3520 row =
DaveM@34 3521
DaveM@34 3522 8811
DaveM@34 3523
DaveM@34 3524 Row: 8811, pDepth = 1, loss = 1.000000
DaveM@34 3525
DaveM@34 3526 Decision tree for classification
DaveM@34 3527 1 if spectral_rolloff_median<0.122574 then node 2 elseif spectral_rolloff_median>=0.122574 then node 3 else 8571
DaveM@34 3528 2 class = 8571
DaveM@34 3529 3 class = 8639
DaveM@34 3530
DaveM@34 3531
DaveM@34 3532 row =
DaveM@34 3533
DaveM@34 3534 8431
DaveM@34 3535
DaveM@34 3536 Row: 8431, pDepth = 1, loss = 0.062500
DaveM@34 3537
DaveM@34 3538 Decision tree for classification
DaveM@34 3539 1 if spectral_rms_mean<0.076959 then node 2 elseif spectral_rms_mean>=0.076959 then node 3 else 8228
DaveM@34 3540 2 class = 8228
DaveM@34 3541 3 class = 7946
DaveM@34 3542
DaveM@34 3543
DaveM@34 3544 row =
DaveM@34 3545
DaveM@34 3546 8515
DaveM@34 3547
DaveM@34 3548 Row: 8515, pDepth = 1, loss = 0.142857
DaveM@34 3549
DaveM@34 3550 Decision tree for classification
DaveM@34 3551 1 if gfcc_mean_3<0.478753 then node 2 elseif gfcc_mean_3>=0.478753 then node 3 else 7558
DaveM@34 3552 2 class = 7558
DaveM@34 3553 3 if dissonance_min<0.453499 then node 4 elseif dissonance_min>=0.453499 then node 5 else 8186
DaveM@34 3554 4 class = 7558
DaveM@34 3555 5 class = 8186
DaveM@34 3556
DaveM@34 3557
DaveM@34 3558 row =
DaveM@34 3559
DaveM@34 3560 6797
DaveM@34 3561
DaveM@34 3562 Row: 6797, pDepth = 1, loss = 1.000000
DaveM@34 3563
DaveM@34 3564 Decision tree for classification
DaveM@34 3565 1 if gfcc_mean_3<0.478753 then node 2 elseif gfcc_mean_3>=0.478753 then node 3 else 7558
DaveM@34 3566 2 class = 7558
DaveM@34 3567 3 if dissonance_min<0.453499 then node 4 elseif dissonance_min>=0.453499 then node 5 else 8186
DaveM@34 3568 4 class = 7558
DaveM@34 3569 5 class = 8186
DaveM@34 3570
DaveM@34 3571
DaveM@34 3572 row =
DaveM@34 3573
DaveM@34 3574 8500
DaveM@34 3575
DaveM@34 3576 Row: 8500, pDepth = 1, loss = 1.000000
DaveM@34 3577
DaveM@34 3578 Decision tree for classification
DaveM@34 3579 1 if gfcc_mean_3<0.478753 then node 2 elseif gfcc_mean_3>=0.478753 then node 3 else 7558
DaveM@34 3580 2 class = 7558
DaveM@34 3581 3 if dissonance_min<0.453499 then node 4 elseif dissonance_min>=0.453499 then node 5 else 8186
DaveM@34 3582 4 class = 7558
DaveM@34 3583 5 class = 8186
DaveM@34 3584
DaveM@34 3585
DaveM@34 3586 row =
DaveM@34 3587
DaveM@34 3588 8788
DaveM@34 3589
DaveM@34 3590 Row: 8788, pDepth = 2, loss = 0.181818
DaveM@34 3591
DaveM@34 3592 Decision tree for classification
DaveM@34 3593 1 if spectral_contrast_median_0<0.425454 then node 2 elseif spectral_contrast_median_0>=0.425454 then node 3 else 8643
DaveM@34 3594 2 class = 8643
DaveM@34 3595 3 if spectral_kurtosis_dmean2<0.0004385 then node 4 elseif spectral_kurtosis_dmean2>=0.0004385 then node 5 else 8303
DaveM@34 3596 4 class = 8643
DaveM@34 3597 5 class = 8303
DaveM@34 3598
DaveM@34 3599
DaveM@34 3600 row =
DaveM@34 3601
DaveM@34 3602 8826
DaveM@34 3603
DaveM@34 3604 Row: 8826, pDepth = 2, loss = 0.103896
DaveM@34 3605
DaveM@34 3606 Decision tree for classification
DaveM@34 3607 1 if spectral_contrast_median_2<0.304088 then node 2 elseif spectral_contrast_median_2>=0.304088 then node 3 else 8620
DaveM@34 3608 2 class = 8620
DaveM@34 3609 3 class = 8778
DaveM@34 3610
DaveM@34 3611
DaveM@34 3612 row =
DaveM@34 3613
DaveM@34 3614 8721
DaveM@34 3615
DaveM@34 3616 Row: 8721, pDepth = 2, loss = 0.107143
DaveM@34 3617
DaveM@34 3618 Decision tree for classification
DaveM@34 3619 1 if spectral_contrast_dmean2_4<0.105466 then node 2 elseif spectral_contrast_dmean2_4>=0.105466 then node 3 else 8576
DaveM@34 3620 2 class = 8576
DaveM@34 3621 3 class = 8591
DaveM@34 3622
DaveM@34 3623
DaveM@34 3624 row =
DaveM@34 3625
DaveM@34 3626 8861
DaveM@34 3627
DaveM@34 3628 Row: 8861, pDepth = 2, loss = 0.111111
DaveM@34 3629
DaveM@34 3630 Decision tree for classification
DaveM@34 3631 1 if beats_loudness_band_ratio_min_0<0.1576 then node 2 elseif beats_loudness_band_ratio_min_0>=0.1576 then node 3 else 8733
DaveM@34 3632 2 class = 8733
DaveM@34 3633 3 class = 8625
DaveM@34 3634
DaveM@34 3635
DaveM@34 3636 row =
DaveM@34 3637
DaveM@34 3638 8761
DaveM@34 3639
DaveM@34 3640 Row: 8761, pDepth = 1, loss = 1.000000
DaveM@34 3641
DaveM@34 3642 Decision tree for classification
DaveM@34 3643 1 if beats_loudness_band_ratio_min_0<0.1576 then node 2 elseif beats_loudness_band_ratio_min_0>=0.1576 then node 3 else 8733
DaveM@34 3644 2 class = 8733
DaveM@34 3645 3 class = 8625
DaveM@34 3646
DaveM@34 3647
DaveM@34 3648 row =
DaveM@34 3649
DaveM@34 3650 8786
DaveM@34 3651
DaveM@34 3652 Row: 8786, pDepth = 1, loss = 0.053571
DaveM@34 3653
DaveM@34 3654 Decision tree for classification
DaveM@34 3655 1 if beats_loudness_band_ratio_min_2<0.396462 then node 2 elseif beats_loudness_band_ratio_min_2>=0.396462 then node 3 else 8664
DaveM@34 3656 2 class = 8664
DaveM@34 3657 3 class = 8452
DaveM@34 3658
DaveM@34 3659
DaveM@34 3660 row =
DaveM@34 3661
DaveM@34 3662 8690
DaveM@34 3663
DaveM@34 3664 Row: 8690, pDepth = 1, loss = 1.000000
DaveM@34 3665
DaveM@34 3666 Decision tree for classification
DaveM@34 3667 1 if beats_loudness_band_ratio_min_2<0.396462 then node 2 elseif beats_loudness_band_ratio_min_2>=0.396462 then node 3 else 8664
DaveM@34 3668 2 class = 8664
DaveM@34 3669 3 class = 8452
DaveM@34 3670
DaveM@34 3671
DaveM@34 3672 row =
DaveM@34 3673
DaveM@34 3674 8731
DaveM@34 3675
DaveM@34 3676 Row: 8731, pDepth = 3, loss = 0.078431
DaveM@34 3677
DaveM@34 3678 Decision tree for classification
DaveM@34 3679 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3680 2 class = 8522
DaveM@34 3681 3 class = 8547
DaveM@34 3682
DaveM@34 3683
DaveM@34 3684 row =
DaveM@34 3685
DaveM@34 3686 8608
DaveM@34 3687
DaveM@34 3688 Row: 8608, pDepth = 1, loss = 1.000000
DaveM@34 3689
DaveM@34 3690 Decision tree for classification
DaveM@34 3691 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3692 2 class = 8522
DaveM@34 3693 3 class = 8547
DaveM@34 3694
DaveM@34 3695
DaveM@34 3696 row =
DaveM@34 3697
DaveM@34 3698 8772
DaveM@34 3699
DaveM@34 3700 Row: 8772, pDepth = 0, loss = 1.000000
DaveM@34 3701
DaveM@34 3702 Decision tree for classification
DaveM@34 3703 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3704 2 class = 8522
DaveM@34 3705 3 class = 8547
DaveM@34 3706
DaveM@34 3707
DaveM@34 3708 row =
DaveM@34 3709
DaveM@34 3710 8619
DaveM@34 3711
DaveM@34 3712 Row: 8619, pDepth = 1, loss = 1.000000
DaveM@34 3713
DaveM@34 3714 Decision tree for classification
DaveM@34 3715 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3716 2 class = 8522
DaveM@34 3717 3 class = 8547
DaveM@34 3718
DaveM@34 3719
DaveM@34 3720 row =
DaveM@34 3721
DaveM@34 3722 8725
DaveM@34 3723
DaveM@34 3724 Row: 8725, pDepth = 1, loss = 1.000000
DaveM@34 3725
DaveM@34 3726 Decision tree for classification
DaveM@34 3727 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3728 2 class = 8522
DaveM@34 3729 3 class = 8547
DaveM@34 3730
DaveM@34 3731
DaveM@34 3732 row =
DaveM@34 3733
DaveM@34 3734 3025
DaveM@34 3735
DaveM@34 3736 Row: 3025, pDepth = 1, loss = 1.000000
DaveM@34 3737
DaveM@34 3738 Decision tree for classification
DaveM@34 3739 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3740 2 class = 8522
DaveM@34 3741 3 class = 8547
DaveM@34 3742
DaveM@34 3743
DaveM@34 3744 row =
DaveM@34 3745
DaveM@34 3746 5585
DaveM@34 3747
DaveM@34 3748 Row: 5585, pDepth = 0, loss = 1.000000
DaveM@34 3749
DaveM@34 3750 Decision tree for classification
DaveM@34 3751 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3752 2 class = 8522
DaveM@34 3753 3 class = 8547
DaveM@34 3754
DaveM@34 3755
DaveM@34 3756 row =
DaveM@34 3757
DaveM@34 3758 7034
DaveM@34 3759
DaveM@34 3760 Row: 7034, pDepth = 0, loss = 1.000000
DaveM@34 3761
DaveM@34 3762 Decision tree for classification
DaveM@34 3763 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3764 2 class = 8522
DaveM@34 3765 3 class = 8547
DaveM@34 3766
DaveM@34 3767
DaveM@34 3768 row =
DaveM@34 3769
DaveM@34 3770 7190
DaveM@34 3771
DaveM@34 3772 Row: 7190, pDepth = 0, loss = 1.000000
DaveM@34 3773
DaveM@34 3774 Decision tree for classification
DaveM@34 3775 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3776 2 class = 8522
DaveM@34 3777 3 class = 8547
DaveM@34 3778
DaveM@34 3779
DaveM@34 3780 row =
DaveM@34 3781
DaveM@34 3782 5191
DaveM@34 3783
DaveM@34 3784 Row: 5191, pDepth = 1, loss = 1.000000
DaveM@34 3785
DaveM@34 3786 Decision tree for classification
DaveM@34 3787 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3788 2 class = 8522
DaveM@34 3789 3 class = 8547
DaveM@34 3790
DaveM@34 3791
DaveM@34 3792 row =
DaveM@34 3793
DaveM@34 3794 7093
DaveM@34 3795
DaveM@34 3796 Row: 7093, pDepth = 0, loss = 1.000000
DaveM@34 3797
DaveM@34 3798 Decision tree for classification
DaveM@34 3799 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3800 2 class = 8522
DaveM@34 3801 3 class = 8547
DaveM@34 3802
DaveM@34 3803
DaveM@34 3804 row =
DaveM@34 3805
DaveM@34 3806 7882
DaveM@34 3807
DaveM@34 3808 Row: 7882, pDepth = 0, loss = 1.000000
DaveM@34 3809
DaveM@34 3810 Decision tree for classification
DaveM@34 3811 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3812 2 class = 8522
DaveM@34 3813 3 class = 8547
DaveM@34 3814
DaveM@34 3815
DaveM@34 3816 row =
DaveM@34 3817
DaveM@34 3818 7908
DaveM@34 3819
DaveM@34 3820 Row: 7908, pDepth = 1, loss = 1.000000
DaveM@34 3821
DaveM@34 3822 Decision tree for classification
DaveM@34 3823 1 if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
DaveM@34 3824 2 class = 8522
DaveM@34 3825 3 class = 8547
DaveM@34 3826
DaveM@34 3827
DaveM@34 3828 row =
DaveM@34 3829
DaveM@34 3830 6660
DaveM@34 3831
DaveM@34 3832 Row: 6660, pDepth = 1, loss = 0.111111
DaveM@34 3833
DaveM@34 3834 Decision tree for classification
DaveM@34 3835 1 if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
DaveM@34 3836 2 class = 4035
DaveM@34 3837 3 class = 6376
DaveM@34 3838
DaveM@34 3839
DaveM@34 3840 row =
DaveM@34 3841
DaveM@34 3842 6662
DaveM@34 3843
DaveM@34 3844 Row: 6662, pDepth = 1, loss = 1.000000
DaveM@34 3845
DaveM@34 3846 Decision tree for classification
DaveM@34 3847 1 if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
DaveM@34 3848 2 class = 4035
DaveM@34 3849 3 class = 6376
DaveM@34 3850
DaveM@34 3851
DaveM@34 3852 row =
DaveM@34 3853
DaveM@34 3854 6659
DaveM@34 3855
DaveM@34 3856 Row: 6659, pDepth = 1, loss = 1.000000
DaveM@34 3857
DaveM@34 3858 Decision tree for classification
DaveM@34 3859 1 if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
DaveM@34 3860 2 class = 4035
DaveM@34 3861 3 class = 6376
DaveM@34 3862
DaveM@34 3863
DaveM@34 3864 row =
DaveM@34 3865
DaveM@34 3866 7744
DaveM@34 3867
DaveM@34 3868 Row: 7744, pDepth = 1, loss = 1.000000
DaveM@34 3869
DaveM@34 3870 Decision tree for classification
DaveM@34 3871 1 if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
DaveM@34 3872 2 class = 4035
DaveM@34 3873 3 class = 6376
DaveM@34 3874
DaveM@34 3875
DaveM@34 3876 row =
DaveM@34 3877
DaveM@34 3878 7702
DaveM@34 3879
DaveM@34 3880 Row: 7702, pDepth = 1, loss = 1.000000
DaveM@34 3881
DaveM@34 3882 Decision tree for classification
DaveM@34 3883 1 if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
DaveM@34 3884 2 class = 4035
DaveM@34 3885 3 class = 6376
DaveM@34 3886
DaveM@34 3887
DaveM@34 3888 row =
DaveM@34 3889
DaveM@34 3890 7955
DaveM@34 3891
DaveM@34 3892 Row: 7955, pDepth = 1, loss = 1.000000
DaveM@34 3893
DaveM@34 3894 Decision tree for classification
DaveM@34 3895 1 if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
DaveM@34 3896 2 class = 4035
DaveM@34 3897 3 class = 6376
DaveM@34 3898
DaveM@34 3899
DaveM@34 3900 row =
DaveM@34 3901
DaveM@34 3902 8069
DaveM@34 3903
DaveM@34 3904 Row: 8069, pDepth = 1, loss = 1.000000
DaveM@34 3905
DaveM@34 3906 Decision tree for classification
DaveM@34 3907 1 if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
DaveM@34 3908 2 class = 4035
DaveM@34 3909 3 class = 6376
DaveM@34 3910
DaveM@34 3911
DaveM@34 3912 row =
DaveM@34 3913
DaveM@34 3914 8179
DaveM@34 3915
DaveM@34 3916 Row: 8179, pDepth = 1, loss = 1.000000
DaveM@34 3917
DaveM@34 3918 Decision tree for classification
DaveM@34 3919 1 if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
DaveM@34 3920 2 class = 4035
DaveM@34 3921 3 class = 6376
DaveM@34 3922
DaveM@34 3923
DaveM@34 3924 row =
DaveM@34 3925
DaveM@34 3926 5426
DaveM@34 3927
DaveM@34 3928 Row: 5426, pDepth = 1, loss = 1.000000
DaveM@34 3929
DaveM@34 3930 Decision tree for classification
DaveM@34 3931 1 if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
DaveM@34 3932 2 class = 4035
DaveM@34 3933 3 class = 6376
DaveM@34 3934
DaveM@34 3935
DaveM@34 3936 row =
DaveM@34 3937
DaveM@34 3938 7248
DaveM@34 3939
DaveM@34 3940 Row: 7248, pDepth = 0, loss = 1.000000
DaveM@34 3941
DaveM@34 3942 Decision tree for classification
DaveM@34 3943 1 if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
DaveM@34 3944 2 class = 4035
DaveM@34 3945 3 class = 6376
DaveM@34 3946
DaveM@34 3947
DaveM@34 3948 row =
DaveM@34 3949
DaveM@34 3950 3551
DaveM@34 3951
DaveM@34 3952 Row: 3551, pDepth = 0, loss = 1.000000
DaveM@34 3953
DaveM@34 3954 Decision tree for classification
DaveM@34 3955 1 if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
DaveM@34 3956 2 class = 4035
DaveM@34 3957 3 class = 6376
DaveM@34 3958
DaveM@34 3959
DaveM@34 3960 row =
DaveM@34 3961
DaveM@34 3962 7619
DaveM@34 3963
DaveM@34 3964 Row: 7619, pDepth = 1, loss = 0.075000
DaveM@34 3965
DaveM@34 3966 Decision tree for classification
DaveM@34 3967 1 if tristimulus_var_0<0.25687 then node 2 elseif tristimulus_var_0>=0.25687 then node 3 else 6997
DaveM@34 3968 2 class = 6500
DaveM@34 3969 3 class = 6997
DaveM@34 3970
DaveM@34 3971
DaveM@34 3972 row =
DaveM@34 3973
DaveM@34 3974 6045
DaveM@34 3975
DaveM@34 3976 Row: 6045, pDepth = 0, loss = 1.000000
DaveM@34 3977
DaveM@34 3978 Decision tree for classification
DaveM@34 3979 1 if tristimulus_var_0<0.25687 then node 2 elseif tristimulus_var_0>=0.25687 then node 3 else 6997
DaveM@34 3980 2 class = 6500
DaveM@34 3981 3 class = 6997
DaveM@34 3982
DaveM@34 3983
DaveM@34 3984 row =
DaveM@34 3985
DaveM@34 3986 6570
DaveM@34 3987
DaveM@34 3988 Row: 6570, pDepth = 1, loss = 1.000000
DaveM@34 3989
DaveM@34 3990 Decision tree for classification
DaveM@34 3991 1 if tristimulus_var_0<0.25687 then node 2 elseif tristimulus_var_0>=0.25687 then node 3 else 6997
DaveM@34 3992 2 class = 6500
DaveM@34 3993 3 class = 6997
DaveM@34 3994
DaveM@34 3995
DaveM@34 3996 row =
DaveM@34 3997
DaveM@34 3998 8245
DaveM@34 3999
DaveM@34 4000 Row: 8245, pDepth = 1, loss = 1.000000
DaveM@34 4001
DaveM@34 4002 Decision tree for classification
DaveM@34 4003 1 if tristimulus_var_0<0.25687 then node 2 elseif tristimulus_var_0>=0.25687 then node 3 else 6997
DaveM@34 4004 2 class = 6500
DaveM@34 4005 3 class = 6997
DaveM@34 4006
DaveM@34 4007
DaveM@34 4008 row =
DaveM@34 4009
DaveM@34 4010 8289
DaveM@34 4011
DaveM@34 4012 Row: 8289, pDepth = 1, loss = 0.142857
DaveM@34 4013
DaveM@34 4014 Decision tree for classification
DaveM@34 4015 1 if pitch_instantaneous_confidence_mean<0.493124 then node 2 elseif pitch_instantaneous_confidence_mean>=0.493124 then node 3 else 7695
DaveM@34 4016 2 class = 7695
DaveM@34 4017 3 class = 8058
DaveM@34 4018
DaveM@34 4019
DaveM@34 4020 row =
DaveM@34 4021
DaveM@34 4022 7078
DaveM@34 4023
DaveM@34 4024 Row: 7078, pDepth = 0, loss = 1.000000
DaveM@34 4025
DaveM@34 4026 Decision tree for classification
DaveM@34 4027 1 if pitch_instantaneous_confidence_mean<0.493124 then node 2 elseif pitch_instantaneous_confidence_mean>=0.493124 then node 3 else 7695
DaveM@34 4028 2 class = 7695
DaveM@34 4029 3 class = 8058
DaveM@34 4030
DaveM@34 4031
DaveM@34 4032 row =
DaveM@34 4033
DaveM@34 4034 7253
DaveM@34 4035
DaveM@34 4036 Row: 7253, pDepth = 0, loss = 1.000000
DaveM@34 4037
DaveM@34 4038 Decision tree for classification
DaveM@34 4039 1 if pitch_instantaneous_confidence_mean<0.493124 then node 2 elseif pitch_instantaneous_confidence_mean>=0.493124 then node 3 else 7695
DaveM@34 4040 2 class = 7695
DaveM@34 4041 3 class = 8058
DaveM@34 4042
DaveM@34 4043
DaveM@34 4044 row =
DaveM@34 4045
DaveM@34 4046 8370
DaveM@34 4047
DaveM@34 4048 Row: 8370, pDepth = 1, loss = 0.058824
DaveM@34 4049
DaveM@34 4050 Decision tree for classification
DaveM@34 4051 1 if mfcc_max_12<0.311522 then node 2 elseif mfcc_max_12>=0.311522 then node 3 else 8292
DaveM@34 4052 2 class = 8292
DaveM@34 4053 3 class = 7864
DaveM@34 4054
DaveM@34 4055
DaveM@34 4056 row =
DaveM@34 4057
DaveM@34 4058 8469
DaveM@34 4059
DaveM@34 4060 Row: 8469, pDepth = 1, loss = 1.000000
DaveM@34 4061
DaveM@34 4062 Decision tree for classification
DaveM@34 4063 1 if mfcc_max_12<0.311522 then node 2 elseif mfcc_max_12>=0.311522 then node 3 else 8292
DaveM@34 4064 2 class = 8292
DaveM@34 4065 3 class = 7864
DaveM@34 4066
DaveM@34 4067
DaveM@34 4068 row =
DaveM@34 4069
DaveM@34 4070 8201
DaveM@34 4071
DaveM@34 4072 Row: 8201, pDepth = 1, loss = 0.125000
DaveM@34 4073
DaveM@34 4074 Decision tree for classification
DaveM@34 4075 1 if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
DaveM@34 4076 2 class = 7464
DaveM@34 4077 3 class = 7626
DaveM@34 4078
DaveM@34 4079
DaveM@34 4080 row =
DaveM@34 4081
DaveM@34 4082 8393
DaveM@34 4083
DaveM@34 4084 Row: 8393, pDepth = 1, loss = 1.000000
DaveM@34 4085
DaveM@34 4086 Decision tree for classification
DaveM@34 4087 1 if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
DaveM@34 4088 2 class = 7464
DaveM@34 4089 3 class = 7626
DaveM@34 4090
DaveM@34 4091
DaveM@34 4092 row =
DaveM@34 4093
DaveM@34 4094 7871
DaveM@34 4095
DaveM@34 4096 Row: 7871, pDepth = 1, loss = 1.000000
DaveM@34 4097
DaveM@34 4098 Decision tree for classification
DaveM@34 4099 1 if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
DaveM@34 4100 2 class = 7464
DaveM@34 4101 3 class = 7626
DaveM@34 4102
DaveM@34 4103
DaveM@34 4104 row =
DaveM@34 4105
DaveM@34 4106 8592
DaveM@34 4107
DaveM@34 4108 Row: 8592, pDepth = 1, loss = 1.000000
DaveM@34 4109
DaveM@34 4110 Decision tree for classification
DaveM@34 4111 1 if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
DaveM@34 4112 2 class = 7464
DaveM@34 4113 3 class = 7626
DaveM@34 4114
DaveM@34 4115
DaveM@34 4116 row =
DaveM@34 4117
DaveM@34 4118 3904
DaveM@34 4119
DaveM@34 4120 Row: 3904, pDepth = 0, loss = 1.000000
DaveM@34 4121
DaveM@34 4122 Decision tree for classification
DaveM@34 4123 1 if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
DaveM@34 4124 2 class = 7464
DaveM@34 4125 3 class = 7626
DaveM@34 4126
DaveM@34 4127
DaveM@34 4128 row =
DaveM@34 4129
DaveM@34 4130 6962
DaveM@34 4131
DaveM@34 4132 Row: 6962, pDepth = 0, loss = 1.000000
DaveM@34 4133
DaveM@34 4134 Decision tree for classification
DaveM@34 4135 1 if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
DaveM@34 4136 2 class = 7464
DaveM@34 4137 3 class = 7626
DaveM@34 4138
DaveM@34 4139
DaveM@34 4140 row =
DaveM@34 4141
DaveM@34 4142 7624
DaveM@34 4143
DaveM@34 4144 Row: 7624, pDepth = 1, loss = 1.000000
DaveM@34 4145
DaveM@34 4146 Decision tree for classification
DaveM@34 4147 1 if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
DaveM@34 4148 2 class = 7464
DaveM@34 4149 3 class = 7626
DaveM@34 4150
DaveM@34 4151
DaveM@34 4152 row =
DaveM@34 4153
DaveM@34 4154 7628
DaveM@34 4155
DaveM@34 4156 Row: 7628, pDepth = 0, loss = 1.000000
DaveM@34 4157
DaveM@34 4158 Decision tree for classification
DaveM@34 4159 1 if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
DaveM@34 4160 2 class = 7464
DaveM@34 4161 3 class = 7626
DaveM@34 4162
DaveM@34 4163
DaveM@34 4164 row =
DaveM@34 4165
DaveM@34 4166 8508
DaveM@34 4167
DaveM@34 4168 Row: 8508, pDepth = 1, loss = 0.089286
DaveM@34 4169
DaveM@34 4170 Decision tree for classification
DaveM@34 4171 1 if beats_loudness_band_ratio_dmean2_5<0.0003065 then node 2 elseif beats_loudness_band_ratio_dmean2_5>=0.0003065 then node 3 else 8410
DaveM@34 4172 2 class = 7671
DaveM@34 4173 3 class = 8410
DaveM@34 4174
DaveM@34 4175
DaveM@34 4176 row =
DaveM@34 4177
DaveM@34 4178 8569
DaveM@34 4179
DaveM@34 4180 Row: 8569, pDepth = 2, loss = 0.102041
DaveM@34 4181
DaveM@34 4182 Decision tree for classification
DaveM@34 4183 1 if beats_loudness_band_ratio_mean_5<0.116919 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.116919 then node 3 else 8268
DaveM@34 4184 2 class = 7677
DaveM@34 4185 3 class = 8268
DaveM@34 4186
DaveM@34 4187
DaveM@34 4188 row =
DaveM@34 4189
DaveM@34 4190 8777
DaveM@34 4191
DaveM@34 4192 Row: 8777, pDepth = 1, loss = 0.058824
DaveM@34 4193
DaveM@34 4194 Decision tree for classification
DaveM@34 4195 1 if max_der_before_max_median<0.563359 then node 2 elseif max_der_before_max_median>=0.563359 then node 3 else 8455
DaveM@34 4196 2 class = 8599
DaveM@34 4197 3 class = 8455
DaveM@34 4198
DaveM@34 4199
DaveM@34 4200 row =
DaveM@34 4201
DaveM@34 4202 8801
DaveM@34 4203
DaveM@34 4204 Row: 8801, pDepth = 1, loss = 0.081633
DaveM@34 4205
DaveM@34 4206 Decision tree for classification
DaveM@34 4207 1 if inharmonicity_dvar<0.299663 then node 2 elseif inharmonicity_dvar>=0.299663 then node 3 else 8481
DaveM@34 4208 2 class = 8481
DaveM@34 4209 3 class = 8444
DaveM@34 4210
DaveM@34 4211
DaveM@34 4212 row =
DaveM@34 4213
DaveM@34 4214 6733
DaveM@34 4215
DaveM@34 4216 Row: 6733, pDepth = 0, loss = 1.000000
DaveM@34 4217
DaveM@34 4218 Decision tree for classification
DaveM@34 4219 1 if inharmonicity_dvar<0.299663 then node 2 elseif inharmonicity_dvar>=0.299663 then node 3 else 8481
DaveM@34 4220 2 class = 8481
DaveM@34 4221 3 class = 8444
DaveM@34 4222
DaveM@34 4223
DaveM@34 4224 row =
DaveM@34 4225
DaveM@34 4226 7333
DaveM@34 4227
DaveM@34 4228 Row: 7333, pDepth = 0, loss = 1.000000
DaveM@34 4229
DaveM@34 4230 Decision tree for classification
DaveM@34 4231 1 if inharmonicity_dvar<0.299663 then node 2 elseif inharmonicity_dvar>=0.299663 then node 3 else 8481
DaveM@34 4232 2 class = 8481
DaveM@34 4233 3 class = 8444
DaveM@34 4234
DaveM@34 4235
DaveM@34 4236 row =
DaveM@34 4237
DaveM@34 4238 6473
DaveM@34 4239
DaveM@34 4240 Row: 6473, pDepth = 0, loss = 1.000000
DaveM@34 4241
DaveM@34 4242 Decision tree for classification
DaveM@34 4243 1 if inharmonicity_dvar<0.299663 then node 2 elseif inharmonicity_dvar>=0.299663 then node 3 else 8481
DaveM@34 4244 2 class = 8481
DaveM@34 4245 3 class = 8444
DaveM@34 4246
DaveM@34 4247
DaveM@34 4248 row =
DaveM@34 4249
DaveM@34 4250 8280
DaveM@34 4251
DaveM@34 4252 Row: 8280, pDepth = 1, loss = 1.000000
DaveM@34 4253
DaveM@34 4254 Decision tree for classification
DaveM@34 4255 1 if inharmonicity_dvar<0.299663 then node 2 elseif inharmonicity_dvar>=0.299663 then node 3 else 8481
DaveM@34 4256 2 class = 8481
DaveM@34 4257 3 class = 8444
DaveM@34 4258
DaveM@34 4259
DaveM@34 4260 row =
DaveM@34 4261
DaveM@34 4262 8425
DaveM@34 4263
DaveM@34 4264 Row: 8425, pDepth = 1, loss = 0.081633
DaveM@34 4265
DaveM@34 4266 Decision tree for classification
DaveM@34 4267 1 if gfcc_dmean_1<0.107409 then node 2 elseif gfcc_dmean_1>=0.107409 then node 3 else 7683
DaveM@34 4268 2 class = 8160
DaveM@34 4269 3 class = 7683
DaveM@34 4270
DaveM@34 4271
DaveM@34 4272 row =
DaveM@34 4273
DaveM@34 4274 8612
DaveM@34 4275
DaveM@34 4276 Row: 8612, pDepth = 1, loss = 0.047619
DaveM@34 4277
DaveM@34 4278 Decision tree for classification
DaveM@34 4279 1 if max_der_before_max_min<0.542158 then node 2 elseif max_der_before_max_min>=0.542158 then node 3 else 8078
DaveM@34 4280 2 class = 8269
DaveM@34 4281 3 class = 8078
DaveM@34 4282
DaveM@34 4283
DaveM@34 4284 row =
DaveM@34 4285
DaveM@34 4286 8299
DaveM@34 4287
DaveM@34 4288 Row: 8299, pDepth = 1, loss = 0.033333
DaveM@34 4289
DaveM@34 4290 Decision tree for classification
DaveM@34 4291 1 if spectral_contrast_mean_4<0.303843 then node 2 elseif spectral_contrast_mean_4>=0.303843 then node 3 else 7954
DaveM@34 4292 2 class = 7954
DaveM@34 4293 3 class = 7753
DaveM@34 4294
DaveM@34 4295
DaveM@34 4296 row =
DaveM@34 4297
DaveM@34 4298 8724
DaveM@34 4299
DaveM@34 4300 Row: 8724, pDepth = 2, loss = 0.111111
DaveM@34 4301
DaveM@34 4302 Decision tree for classification
DaveM@34 4303 1 if frequency_bands_dmean2_1<1.5e-06 then node 2 elseif frequency_bands_dmean2_1>=1.5e-06 then node 3 else 8509
DaveM@34 4304 2 if frequency_bands_dvar2_26<4.5e-06 then node 4 elseif frequency_bands_dvar2_26>=4.5e-06 then node 5 else 8509
DaveM@34 4305 3 class = 8509
DaveM@34 4306 4 class = 8499
DaveM@34 4307 5 class = 8509
DaveM@34 4308
DaveM@34 4309
DaveM@34 4310 row =
DaveM@34 4311
DaveM@34 4312 8205
DaveM@34 4313
DaveM@34 4314 Row: 8205, pDepth = 1, loss = 1.000000
DaveM@34 4315
DaveM@34 4316 Decision tree for classification
DaveM@34 4317 1 if frequency_bands_dmean2_1<1.5e-06 then node 2 elseif frequency_bands_dmean2_1>=1.5e-06 then node 3 else 8509
DaveM@34 4318 2 if frequency_bands_dvar2_26<4.5e-06 then node 4 elseif frequency_bands_dvar2_26>=4.5e-06 then node 5 else 8509
DaveM@34 4319 3 class = 8509
DaveM@34 4320 4 class = 8499
DaveM@34 4321 5 class = 8509
DaveM@34 4322
DaveM@34 4323
DaveM@34 4324 row =
DaveM@34 4325
DaveM@34 4326 8377
DaveM@34 4327
DaveM@34 4328 Row: 8377, pDepth = 1, loss = 1.000000
DaveM@34 4329
DaveM@34 4330 Decision tree for classification
DaveM@34 4331 1 if frequency_bands_dmean2_1<1.5e-06 then node 2 elseif frequency_bands_dmean2_1>=1.5e-06 then node 3 else 8509
DaveM@34 4332 2 if frequency_bands_dvar2_26<4.5e-06 then node 4 elseif frequency_bands_dvar2_26>=4.5e-06 then node 5 else 8509
DaveM@34 4333 3 class = 8509
DaveM@34 4334 4 class = 8499
DaveM@34 4335 5 class = 8509
DaveM@34 4336
DaveM@34 4337
DaveM@34 4338 row =
DaveM@34 4339
DaveM@34 4340 6448
DaveM@34 4341
DaveM@34 4342 Row: 6448, pDepth = 1, loss = 1.000000
DaveM@34 4343
DaveM@34 4344 Decision tree for classification
DaveM@34 4345 1 if frequency_bands_dmean2_1<1.5e-06 then node 2 elseif frequency_bands_dmean2_1>=1.5e-06 then node 3 else 8509
DaveM@34 4346 2 if frequency_bands_dvar2_26<4.5e-06 then node 4 elseif frequency_bands_dvar2_26>=4.5e-06 then node 5 else 8509
DaveM@34 4347 3 class = 8509
DaveM@34 4348 4 class = 8499
DaveM@34 4349 5 class = 8509
DaveM@34 4350
DaveM@34 4351
DaveM@34 4352 row =
DaveM@34 4353
DaveM@34 4354 8588
DaveM@34 4355
DaveM@34 4356 Row: 8588, pDepth = 1, loss = 0.022727
DaveM@34 4357
DaveM@34 4358 Decision tree for classification
DaveM@34 4359 1 if spectral_contrast_max_2<0.244373 then node 2 elseif spectral_contrast_max_2>=0.244373 then node 3 else 7832
DaveM@34 4360 2 class = 7942
DaveM@34 4361 3 class = 7832
DaveM@34 4362
DaveM@34 4363
DaveM@34 4364 row =
DaveM@34 4365
DaveM@34 4366 7015
DaveM@34 4367
DaveM@34 4368 Row: 7015, pDepth = 1, loss = 1.000000
DaveM@34 4369
DaveM@34 4370 Decision tree for classification
DaveM@34 4371 1 if spectral_contrast_max_2<0.244373 then node 2 elseif spectral_contrast_max_2>=0.244373 then node 3 else 7832
DaveM@34 4372 2 class = 7942
DaveM@34 4373 3 class = 7832
DaveM@34 4374
DaveM@34 4375
DaveM@34 4376 row =
DaveM@34 4377
DaveM@34 4378 7659
DaveM@34 4379
DaveM@34 4380 Row: 7659, pDepth = 1, loss = 1.000000
DaveM@34 4381
DaveM@34 4382 Decision tree for classification
DaveM@34 4383 1 if spectral_contrast_max_2<0.244373 then node 2 elseif spectral_contrast_max_2>=0.244373 then node 3 else 7832
DaveM@34 4384 2 class = 7942
DaveM@34 4385 3 class = 7832
DaveM@34 4386
DaveM@34 4387
DaveM@34 4388 row =
DaveM@34 4389
DaveM@34 4390 8501
DaveM@34 4391
DaveM@34 4392 Row: 8501, pDepth = 1, loss = 0.023810
DaveM@34 4393
DaveM@34 4394 Decision tree for classification
DaveM@34 4395 1 if barkbands_mean_26<0.0001275 then node 2 elseif barkbands_mean_26>=0.0001275 then node 3 else 7668
DaveM@34 4396 2 class = 7668
DaveM@34 4397 3 class = 8122
DaveM@34 4398
DaveM@34 4399
DaveM@34 4400 row =
DaveM@34 4401
DaveM@34 4402 8535
DaveM@34 4403
DaveM@34 4404 Row: 8535, pDepth = 1, loss = 0.080000
DaveM@34 4405
DaveM@34 4406 Decision tree for classification
DaveM@34 4407 1 if beats_loudness_band_ratio_min_5<0.279003 then node 2 elseif beats_loudness_band_ratio_min_5>=0.279003 then node 3 else 8353
DaveM@34 4408 2 class = 7750
DaveM@34 4409 3 class = 8353
DaveM@34 4410
DaveM@34 4411
DaveM@34 4412 row =
DaveM@34 4413
DaveM@34 4414 8142
DaveM@34 4415
DaveM@34 4416 Row: 8142, pDepth = 1, loss = 0.162791
DaveM@34 4417
DaveM@34 4418 Decision tree for classification
DaveM@34 4419 1 if barkbands_var_17<1.2e-05 then node 2 elseif barkbands_var_17>=1.2e-05 then node 3 else 7798
DaveM@34 4420 2 if frequency_bands_dmean2_12<0.0042235 then node 4 elseif frequency_bands_dmean2_12>=0.0042235 then node 5 else 7798
DaveM@34 4421 3 class = 7917
DaveM@34 4422 4 if frequency_bands_var_9<1.5e-05 then node 6 elseif frequency_bands_var_9>=1.5e-05 then node 7 else 7798
DaveM@34 4423 5 class = 7917
DaveM@34 4424 6 if frequency_bands_dmean2_12<8.35e-05 then node 8 elseif frequency_bands_dmean2_12>=8.35e-05 then node 9 else 7798
DaveM@34 4425 7 class = 7798
DaveM@34 4426 8 class = 7798
DaveM@34 4427 9 if frequency_bands_dmean2_12<0.0019425 then node 10 elseif frequency_bands_dmean2_12>=0.0019425 then node 11 else 7917
DaveM@34 4428 10 class = 7917
DaveM@34 4429 11 class = 7798
DaveM@34 4430
DaveM@34 4431
DaveM@34 4432 row =
DaveM@34 4433
DaveM@34 4434 8368
DaveM@34 4435
DaveM@34 4436 Row: 8368, pDepth = 1, loss = 1.000000
DaveM@34 4437
DaveM@34 4438 Decision tree for classification
DaveM@34 4439 1 if barkbands_var_17<1.2e-05 then node 2 elseif barkbands_var_17>=1.2e-05 then node 3 else 7798
DaveM@34 4440 2 if frequency_bands_dmean2_12<0.0042235 then node 4 elseif frequency_bands_dmean2_12>=0.0042235 then node 5 else 7798
DaveM@34 4441 3 class = 7917
DaveM@34 4442 4 if frequency_bands_var_9<1.5e-05 then node 6 elseif frequency_bands_var_9>=1.5e-05 then node 7 else 7798
DaveM@34 4443 5 class = 7917
DaveM@34 4444 6 if frequency_bands_dmean2_12<8.35e-05 then node 8 elseif frequency_bands_dmean2_12>=8.35e-05 then node 9 else 7798
DaveM@34 4445 7 class = 7798
DaveM@34 4446 8 class = 7798
DaveM@34 4447 9 if frequency_bands_dmean2_12<0.0019425 then node 10 elseif frequency_bands_dmean2_12>=0.0019425 then node 11 else 7917
DaveM@34 4448 10 class = 7917
DaveM@34 4449 11 class = 7798
DaveM@34 4450
DaveM@34 4451
DaveM@34 4452 row =
DaveM@34 4453
DaveM@34 4454 8382
DaveM@34 4455
DaveM@34 4456 Row: 8382, pDepth = 1, loss = 0.115385
DaveM@34 4457
DaveM@34 4458 Decision tree for classification
DaveM@34 4459 1 if frequency_bands_dvar2_10<5.15e-05 then node 2 elseif frequency_bands_dvar2_10>=5.15e-05 then node 3 else 6636
DaveM@34 4460 2 if barkbands_var_4<0.0031125 then node 4 elseif barkbands_var_4>=0.0031125 then node 5 else 6636
DaveM@34 4461 3 if barkbands_var_4<1.2e-05 then node 6 elseif barkbands_var_4>=1.2e-05 then node 7 else 7101
DaveM@34 4462 4 class = 6636
DaveM@34 4463 5 class = 7101
DaveM@34 4464 6 class = 6636
DaveM@34 4465 7 class = 7101
DaveM@34 4466
DaveM@34 4467
DaveM@34 4468 row =
DaveM@34 4469
DaveM@34 4470 8658
DaveM@34 4471
DaveM@34 4472 Row: 8658, pDepth = 1, loss = 0.090909
DaveM@34 4473
DaveM@34 4474 Decision tree for classification
DaveM@34 4475 1 if spectral_contrast_var_2<0.012531 then node 2 elseif spectral_contrast_var_2>=0.012531 then node 3 else 8274
DaveM@34 4476 2 class = 8445
DaveM@34 4477 3 class = 8274
DaveM@34 4478
DaveM@34 4479
DaveM@34 4480 row =
DaveM@34 4481
DaveM@34 4482 5730
DaveM@34 4483
DaveM@34 4484 Row: 5730, pDepth = 1, loss = 0.074074
DaveM@34 4485
DaveM@34 4486 Decision tree for classification
DaveM@34 4487 1 if mfcc_dmean_0<0.137688 then node 2 elseif mfcc_dmean_0>=0.137688 then node 3 else 4773
DaveM@34 4488 2 class = 2875
DaveM@34 4489 3 class = 4773
DaveM@34 4490
DaveM@34 4491
DaveM@34 4492 row =
DaveM@34 4493
DaveM@34 4494 8038
DaveM@34 4495
DaveM@34 4496 Row: 8038, pDepth = 1, loss = 0.027778
DaveM@34 4497
DaveM@34 4498 Decision tree for classification
DaveM@34 4499 1 if silence_rate_60dB_dvar2<0.184306 then node 2 elseif silence_rate_60dB_dvar2>=0.184306 then node 3 else 7209
DaveM@34 4500 2 class = 7209
DaveM@34 4501 3 class = 6792
DaveM@34 4502
DaveM@34 4503
DaveM@34 4504 row =
DaveM@34 4505
DaveM@34 4506 8740
DaveM@34 4507
DaveM@34 4508 Row: 8740, pDepth = 2, loss = 0.181818
DaveM@34 4509
DaveM@34 4510 Decision tree for classification
DaveM@34 4511 1 if frequency_bands_dmean2_4<0.000412 then node 2 elseif frequency_bands_dmean2_4>=0.000412 then node 3 else 8496
DaveM@34 4512 2 class = 8496
DaveM@34 4513 3 if spectral_energyband_high_dvar2<0.002833 then node 4 elseif spectral_energyband_high_dvar2>=0.002833 then node 5 else 8560
DaveM@34 4514 4 if frequency_bands_dmean2_4<0.0095645 then node 6 elseif frequency_bands_dmean2_4>=0.0095645 then node 7 else 8560
DaveM@34 4515 5 class = 8496
DaveM@34 4516 6 if barkbands_dvar2_7<5.35e-05 then node 8 elseif barkbands_dvar2_7>=5.35e-05 then node 9 else 8560
DaveM@34 4517 7 class = 8560
DaveM@34 4518 8 class = 8560
DaveM@34 4519 9 class = 8496
DaveM@34 4520
DaveM@34 4521
DaveM@34 4522 row =
DaveM@34 4523
DaveM@34 4524 8758
DaveM@34 4525
DaveM@34 4526 Row: 8758, pDepth = 1, loss = 0.029412
DaveM@34 4527
DaveM@34 4528 Decision tree for classification
DaveM@34 4529 1 if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
DaveM@34 4530 2 class = 8655
DaveM@34 4531 3 class = 8159
DaveM@34 4532
DaveM@34 4533
DaveM@34 4534 row =
DaveM@34 4535
DaveM@34 4536 7323
DaveM@34 4537
DaveM@34 4538 Row: 7323, pDepth = 1, loss = 1.000000
DaveM@34 4539
DaveM@34 4540 Decision tree for classification
DaveM@34 4541 1 if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
DaveM@34 4542 2 class = 8655
DaveM@34 4543 3 class = 8159
DaveM@34 4544
DaveM@34 4545
DaveM@34 4546 row =
DaveM@34 4547
DaveM@34 4548 7958
DaveM@34 4549
DaveM@34 4550 Row: 7958, pDepth = 1, loss = 1.000000
DaveM@34 4551
DaveM@34 4552 Decision tree for classification
DaveM@34 4553 1 if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
DaveM@34 4554 2 class = 8655
DaveM@34 4555 3 class = 8159
DaveM@34 4556
DaveM@34 4557
DaveM@34 4558 row =
DaveM@34 4559
DaveM@34 4560 8594
DaveM@34 4561
DaveM@34 4562 Row: 8594, pDepth = 0, loss = 1.000000
DaveM@34 4563
DaveM@34 4564 Decision tree for classification
DaveM@34 4565 1 if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
DaveM@34 4566 2 class = 8655
DaveM@34 4567 3 class = 8159
DaveM@34 4568
DaveM@34 4569
DaveM@34 4570 row =
DaveM@34 4571
DaveM@34 4572 8718
DaveM@34 4573
DaveM@34 4574 Row: 8718, pDepth = 0, loss = 1.000000
DaveM@34 4575
DaveM@34 4576 Decision tree for classification
DaveM@34 4577 1 if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
DaveM@34 4578 2 class = 8655
DaveM@34 4579 3 class = 8159
DaveM@34 4580
DaveM@34 4581
DaveM@34 4582 row =
DaveM@34 4583
DaveM@34 4584 8396
DaveM@34 4585
DaveM@34 4586 Row: 8396, pDepth = 1, loss = 1.000000
DaveM@34 4587
DaveM@34 4588 Decision tree for classification
DaveM@34 4589 1 if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
DaveM@34 4590 2 class = 8655
DaveM@34 4591 3 class = 8159
DaveM@34 4592
DaveM@34 4593
DaveM@34 4594 row =
DaveM@34 4595
DaveM@34 4596 8689
DaveM@34 4597
DaveM@34 4598 Row: 8689, pDepth = 1, loss = 0.034483
DaveM@34 4599
DaveM@34 4600 Decision tree for classification
DaveM@34 4601 1 if gfcc_dmean2_3<0.318661 then node 2 elseif gfcc_dmean2_3>=0.318661 then node 3 else 8405
DaveM@34 4602 2 class = 8405
DaveM@34 4603 3 class = 8573
DaveM@34 4604
DaveM@34 4605
DaveM@34 4606 row =
DaveM@34 4607
DaveM@34 4608 8671
DaveM@34 4609
DaveM@34 4610 Row: 8671, pDepth = 1, loss = 0.095238
DaveM@34 4611
DaveM@34 4612 Decision tree for classification
DaveM@34 4613 1 if silence_rate_60dB_mean<0.875215 then node 2 elseif silence_rate_60dB_mean>=0.875215 then node 3 else 8039
DaveM@34 4614 2 class = 8039
DaveM@34 4615 3 class = 7615
DaveM@34 4616
DaveM@34 4617
DaveM@34 4618 row =
DaveM@34 4619
DaveM@34 4620 8694
DaveM@34 4621
DaveM@34 4622 Row: 8694, pDepth = 1, loss = 1.000000
DaveM@34 4623
DaveM@34 4624 Decision tree for classification
DaveM@34 4625 1 if silence_rate_60dB_mean<0.875215 then node 2 elseif silence_rate_60dB_mean>=0.875215 then node 3 else 8039
DaveM@34 4626 2 class = 8039
DaveM@34 4627 3 class = 7615
DaveM@34 4628
DaveM@34 4629
DaveM@34 4630 row =
DaveM@34 4631
DaveM@34 4632 8557
DaveM@34 4633
DaveM@34 4634 Row: 8557, pDepth = 3, loss = 0.150685
DaveM@34 4635
DaveM@34 4636 Decision tree for classification
DaveM@34 4637 1 if spectral_skewness_min<0.963354 then node 2 elseif spectral_skewness_min>=0.963354 then node 3 else 8093
DaveM@34 4638 2 class = 8344
DaveM@34 4639 3 class = 8093
DaveM@34 4640
DaveM@34 4641
DaveM@34 4642 row =
DaveM@34 4643
DaveM@34 4644 8626
DaveM@34 4645
DaveM@34 4646 Row: 8626, pDepth = 1, loss = 0.076923
DaveM@34 4647
DaveM@34 4648 Decision tree for classification
DaveM@34 4649 1 if beats_loudness_band_ratio_mean_3<0.489398 then node 2 elseif beats_loudness_band_ratio_mean_3>=0.489398 then node 3 else 8355
DaveM@34 4650 2 class = 8355
DaveM@34 4651 3 class = 8422
DaveM@34 4652
DaveM@34 4653
DaveM@34 4654 row =
DaveM@34 4655
DaveM@34 4656 8566
DaveM@34 4657
DaveM@34 4658 Row: 8566, pDepth = 1, loss = 0.045455
DaveM@34 4659
DaveM@34 4660 Decision tree for classification
DaveM@34 4661 1 if scvalleys_dvar_5<0.0120155 then node 2 elseif scvalleys_dvar_5>=0.0120155 then node 3 else 8415
DaveM@34 4662 2 class = 8030
DaveM@34 4663 3 class = 8415
DaveM@34 4664
DaveM@34 4665
DaveM@34 4666 row =
DaveM@34 4667
DaveM@34 4668 8813
DaveM@34 4669
DaveM@34 4670 Row: 8813, pDepth = 1, loss = 0.080645
DaveM@34 4671
DaveM@34 4672 Decision tree for classification
DaveM@34 4673 1 if mfcc_max_0<0.625379 then node 2 elseif mfcc_max_0>=0.625379 then node 3 else 8627
DaveM@34 4674 2 class = 8627
DaveM@34 4675 3 class = 8653
DaveM@34 4676
DaveM@34 4677
DaveM@34 4678 row =
DaveM@34 4679
DaveM@34 4680 8603
DaveM@34 4681
DaveM@34 4682 Row: 8603, pDepth = 1, loss = 0.030303
DaveM@34 4683
DaveM@34 4684 Decision tree for classification
DaveM@34 4685 1 if dissonance_dmean<0.036456 then node 2 elseif dissonance_dmean>=0.036456 then node 3 else 7897
DaveM@34 4686 2 class = 7555
DaveM@34 4687 3 class = 7897
DaveM@34 4688
DaveM@34 4689
DaveM@34 4690 row =
DaveM@34 4691
DaveM@34 4692 8780
DaveM@34 4693
DaveM@34 4694 Row: 8780, pDepth = 1, loss = 1.000000
DaveM@34 4695
DaveM@34 4696 Decision tree for classification
DaveM@34 4697 1 if dissonance_dmean<0.036456 then node 2 elseif dissonance_dmean>=0.036456 then node 3 else 7897
DaveM@34 4698 2 class = 7555
DaveM@34 4699 3 class = 7897
DaveM@34 4700
DaveM@34 4701
DaveM@34 4702 row =
DaveM@34 4703
DaveM@34 4704 8789
DaveM@34 4705
DaveM@34 4706 Row: 8789, pDepth = 4, loss = 0.177083
DaveM@34 4707
DaveM@34 4708 Decision tree for classification
DaveM@34 4709 1 if spectral_entropy_dmean<0.0841085 then node 2 elseif spectral_entropy_dmean>=0.0841085 then node 3 else 8624
DaveM@34 4710 2 class = 8339
DaveM@34 4711 3 class = 8624
DaveM@34 4712
DaveM@34 4713
DaveM@34 4714 row =
DaveM@34 4715
DaveM@34 4716 8842
DaveM@34 4717
DaveM@34 4718 Row: 8842, pDepth = 2, loss = 0.153374
DaveM@34 4719
DaveM@34 4720 Decision tree for classification
DaveM@34 4721 1 if scvalleys_min_0<0.437758 then node 2 elseif scvalleys_min_0>=0.437758 then node 3 else 8803
DaveM@34 4722 2 class = 8803
DaveM@34 4723 3 class = 8754
DaveM@34 4724
DaveM@34 4725
DaveM@34 4726 row =
DaveM@34 4727
DaveM@34 4728 8656
DaveM@34 4729
DaveM@34 4730 Row: 8656, pDepth = 1, loss = 0.083333
DaveM@34 4731
DaveM@34 4732 Decision tree for classification
DaveM@34 4733 1 if gfcc_median_1<0.294408 then node 2 elseif gfcc_median_1>=0.294408 then node 3 else 8498
DaveM@34 4734 2 class = 7813
DaveM@34 4735 3 class = 8498
DaveM@34 4736
DaveM@34 4737
DaveM@34 4738 row =
DaveM@34 4739
DaveM@34 4740 8667
DaveM@34 4741
DaveM@34 4742 Row: 8667, pDepth = 1, loss = 1.000000
DaveM@34 4743
DaveM@34 4744 Decision tree for classification
DaveM@34 4745 1 if gfcc_median_1<0.294408 then node 2 elseif gfcc_median_1>=0.294408 then node 3 else 8498
DaveM@34 4746 2 class = 7813
DaveM@34 4747 3 class = 8498
DaveM@34 4748
DaveM@34 4749
DaveM@34 4750 row =
DaveM@34 4751
DaveM@34 4752 8699
DaveM@34 4753
DaveM@34 4754 Row: 8699, pDepth = 1, loss = 0.019608
DaveM@34 4755
DaveM@34 4756 Decision tree for classification
DaveM@34 4757 1 if beats_loudness_band_ratio_mean_5<0.177665 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.177665 then node 3 else 8577
DaveM@34 4758 2 class = 8577
DaveM@34 4759 3 class = 8544
DaveM@34 4760
DaveM@34 4761
DaveM@34 4762 row =
DaveM@34 4763
DaveM@34 4764 8746
DaveM@34 4765
DaveM@34 4766 Row: 8746, pDepth = 1, loss = 1.000000
DaveM@34 4767
DaveM@34 4768 Decision tree for classification
DaveM@34 4769 1 if beats_loudness_band_ratio_mean_5<0.177665 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.177665 then node 3 else 8577
DaveM@34 4770 2 class = 8577
DaveM@34 4771 3 class = 8544
DaveM@34 4772
DaveM@34 4773
DaveM@34 4774 row =
DaveM@34 4775
DaveM@34 4776 8640
DaveM@34 4777
DaveM@34 4778 Row: 8640, pDepth = 1, loss = 1.000000
DaveM@34 4779
DaveM@34 4780 Decision tree for classification
DaveM@34 4781 1 if beats_loudness_band_ratio_mean_5<0.177665 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.177665 then node 3 else 8577
DaveM@34 4782 2 class = 8577
DaveM@34 4783 3 class = 8544
DaveM@34 4784
DaveM@34 4785
DaveM@34 4786 row =
DaveM@34 4787
DaveM@34 4788 8692
DaveM@34 4789
DaveM@34 4790 Row: 8692, pDepth = 1, loss = 1.000000
DaveM@34 4791
DaveM@34 4792 Decision tree for classification
DaveM@34 4793 1 if beats_loudness_band_ratio_mean_5<0.177665 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.177665 then node 3 else 8577
DaveM@34 4794 2 class = 8577
DaveM@34 4795 3 class = 8544
DaveM@34 4796
DaveM@34 4797
DaveM@34 4798 row =
DaveM@34 4799
DaveM@34 4800 8714
DaveM@34 4801
DaveM@34 4802 Row: 8714, pDepth = 1, loss = 0.030769
DaveM@34 4803
DaveM@34 4804 Decision tree for classification
DaveM@34 4805 1 if beats_loudness_band_ratio_mean_0<0.538998 then node 2 elseif beats_loudness_band_ratio_mean_0>=0.538998 then node 3 else 8507
DaveM@34 4806 2 class = 8507
DaveM@34 4807 3 class = 8261
DaveM@34 4808
DaveM@34 4809
DaveM@34 4810 row =
DaveM@34 4811
DaveM@34 4812 8792
DaveM@34 4813
DaveM@34 4814 Row: 8792, pDepth = 2, loss = 0.140845
DaveM@34 4815
DaveM@34 4816 Decision tree for classification
DaveM@34 4817 1 if frequency_bands_dmean2_17<0.0004845 then node 2 elseif frequency_bands_dmean2_17>=0.0004845 then node 3 else 8715
DaveM@34 4818 2 if tristimulus_min_1<0.008642 then node 4 elseif tristimulus_min_1>=0.008642 then node 5 else 8715
DaveM@34 4819 3 if frequency_bands_dmean2_17<0.001461 then node 6 elseif frequency_bands_dmean2_17>=0.001461 then node 7 else 8613
DaveM@34 4820 4 if frequency_bands_dmean_16<0.000217 then node 8 elseif frequency_bands_dmean_16>=0.000217 then node 9 else 8715
DaveM@34 4821 5 class = 8613
DaveM@34 4822 6 if frequency_bands_dmean_16<0.0001365 then node 10 elseif frequency_bands_dmean_16>=0.0001365 then node 11 else 8613
DaveM@34 4823 7 if frequency_bands_dmean_16<0.0025025 then node 12 elseif frequency_bands_dmean_16>=0.0025025 then node 13 else 8715
DaveM@34 4824 8 class = 8715
DaveM@34 4825 9 if erb_bands_dvar2_7<5.5e-06 then node 14 elseif erb_bands_dvar2_7>=5.5e-06 then node 15 else 8715
DaveM@34 4826 10 class = 8715
DaveM@34 4827 11 class = 8613
DaveM@34 4828 12 if frequency_bands_dmean_16<0.0011695 then node 16 elseif frequency_bands_dmean_16>=0.0011695 then node 17 else 8715
DaveM@34 4829 13 class = 8613
DaveM@34 4830 14 if tristimulus_min_1<0.000348 then node 18 elseif tristimulus_min_1>=0.000348 then node 19 else 8613
DaveM@34 4831 15 if frequency_bands_dmean_16<0.0002655 then node 20 elseif frequency_bands_dmean_16>=0.0002655 then node 21 else 8715
DaveM@34 4832 16 if erb_bands_dvar2_7<7.65e-05 then node 22 elseif erb_bands_dvar2_7>=7.65e-05 then node 23 else 8715
DaveM@34 4833 17 class = 8715
DaveM@34 4834 18 if frequency_bands_dmean2_17<0.000276 then node 24 elseif frequency_bands_dmean2_17>=0.000276 then node 25 else 8613
DaveM@34 4835 19 class = 8715
DaveM@34 4836 20 class = 8613
DaveM@34 4837 21 class = 8715
DaveM@34 4838 22 if frequency_bands_dmean2_17<0.0113015 then node 26 elseif frequency_bands_dmean2_17>=0.0113015 then node 27 else 8715
DaveM@34 4839 23 class = 8715
DaveM@34 4840 24 if frequency_bands_dmean_16<0.000512 then node 28 elseif frequency_bands_dmean_16>=0.000512 then node 29 else 8715
DaveM@34 4841 25 class = 8613
DaveM@34 4842 26 if frequency_bands_dmean2_17<0.0050595 then node 30 elseif frequency_bands_dmean2_17>=0.0050595 then node 31 else 8613
DaveM@34 4843 27 class = 8715
DaveM@34 4844 28 class = 8613
DaveM@34 4845 29 class = 8715
DaveM@34 4846 30 class = 8715
DaveM@34 4847 31 class = 8613
DaveM@34 4848
DaveM@34 4849
DaveM@34 4850 row =
DaveM@34 4851
DaveM@34 4852 8378
DaveM@34 4853
DaveM@34 4854 Row: 8378, pDepth = 1, loss = 0.108108
DaveM@34 4855
DaveM@34 4856 Decision tree for classification
DaveM@34 4857 1 if first_peak_spread_min<0.099624 then node 2 elseif first_peak_spread_min>=0.099624 then node 3 else 8129
DaveM@34 4858 2 class = 8129
DaveM@34 4859 3 class = 7757
DaveM@34 4860
DaveM@34 4861
DaveM@34 4862 row =
DaveM@34 4863
DaveM@34 4864 8531
DaveM@34 4865
DaveM@34 4866 Row: 8531, pDepth = 1, loss = 0.093023
DaveM@34 4867
DaveM@34 4868 Decision tree for classification
DaveM@34 4869 1 if beats_loudness_band_ratio_max_0<1.5e-06 then node 2 elseif beats_loudness_band_ratio_max_0>=1.5e-06 then node 3 else 8476
DaveM@34 4870 2 class = 7554
DaveM@34 4871 3 class = 8476
DaveM@34 4872
DaveM@34 4873
DaveM@34 4874 row =
DaveM@34 4875
DaveM@34 4876 8604
DaveM@34 4877
DaveM@34 4878 Row: 8604, pDepth = 1, loss = 0.093750
DaveM@34 4879
DaveM@34 4880 Decision tree for classification
DaveM@34 4881 1 if beats_loudness_band_ratio_median_0<0.499813 then node 2 elseif beats_loudness_band_ratio_median_0>=0.499813 then node 3 else 8225
DaveM@34 4882 2 class = 8225
DaveM@34 4883 3 class = 7956
DaveM@34 4884
DaveM@34 4885
DaveM@34 4886 row =
DaveM@34 4887
DaveM@34 4888 8702
DaveM@34 4889
DaveM@34 4890 Row: 8702, pDepth = 3, loss = 0.130952
DaveM@34 4891
DaveM@34 4892 Decision tree for classification
DaveM@34 4893 1 if max_der_before_max_mean<0.547416 then node 2 elseif max_der_before_max_mean>=0.547416 then node 3 else 8466
DaveM@34 4894 2 class = 8466
DaveM@34 4895 3 class = 8340
DaveM@34 4896
DaveM@34 4897
DaveM@34 4898 row =
DaveM@34 4899
DaveM@34 4900 8386
DaveM@34 4901
DaveM@34 4902 Row: 8386, pDepth = 2, loss = 0.125000
DaveM@34 4903
DaveM@34 4904 Decision tree for classification
DaveM@34 4905 1 if scvalleys_median_2<0.744365 then node 2 elseif scvalleys_median_2>=0.744365 then node 3 else 8182
DaveM@34 4906 2 class = 8182
DaveM@34 4907 3 class = 7455
DaveM@34 4908
DaveM@34 4909
DaveM@34 4910 row =
DaveM@34 4911
DaveM@34 4912 8862
DaveM@34 4913
DaveM@34 4914 Row: 8862, pDepth = 3, loss = 0.118343
DaveM@34 4915
DaveM@34 4916 Decision tree for classification
DaveM@34 4917 1 if spectral_flux_median<0.020283 then node 2 elseif spectral_flux_median>=0.020283 then node 3 else 8701
DaveM@34 4918 2 if spectral_skewness_median<0.0641445 then node 4 elseif spectral_skewness_median>=0.0641445 then node 5 else 8701
DaveM@34 4919 3 class = 8802
DaveM@34 4920 4 class = 8701
DaveM@34 4921 5 class = 8802
DaveM@34 4922
DaveM@34 4923
DaveM@34 4924 row =
DaveM@34 4925
DaveM@34 4926 8737
DaveM@34 4927
DaveM@34 4928 Row: 8737, pDepth = 2, loss = 0.132530
DaveM@34 4929
DaveM@34 4930 Decision tree for classification
DaveM@34 4931 1 if spectral_rms_max<0.181331 then node 2 elseif spectral_rms_max>=0.181331 then node 3 else 8681
DaveM@34 4932 2 class = 8681
DaveM@34 4933 3 class = 8372
DaveM@34 4934
DaveM@34 4935
DaveM@34 4936 row =
DaveM@34 4937
DaveM@34 4938 8899
DaveM@34 4939
DaveM@34 4940 Row: 8899, pDepth = 1, loss = 0.022727
DaveM@34 4941
DaveM@34 4942 Decision tree for classification
DaveM@34 4943 1 if beats_loudness_band_ratio_var_2<6.5e-06 then node 2 elseif beats_loudness_band_ratio_var_2>=6.5e-06 then node 3 else 8847
DaveM@34 4944 2 class = 8847
DaveM@34 4945 3 class = 8827
DaveM@34 4946
DaveM@34 4947
DaveM@34 4948 row =
DaveM@34 4949
DaveM@34 4950 7673
DaveM@34 4951
DaveM@34 4952 Row: 7673, pDepth = 1, loss = 1.000000
DaveM@34 4953
DaveM@34 4954 Decision tree for classification
DaveM@34 4955 1 if beats_loudness_band_ratio_var_2<6.5e-06 then node 2 elseif beats_loudness_band_ratio_var_2>=6.5e-06 then node 3 else 8847
DaveM@34 4956 2 class = 8847
DaveM@34 4957 3 class = 8827
DaveM@34 4958
DaveM@34 4959
DaveM@34 4960 row =
DaveM@34 4961
DaveM@34 4962 8361
DaveM@34 4963
DaveM@34 4964 Row: 8361, pDepth = 1, loss = 1.000000
DaveM@34 4965
DaveM@34 4966 Decision tree for classification
DaveM@34 4967 1 if beats_loudness_band_ratio_var_2<6.5e-06 then node 2 elseif beats_loudness_band_ratio_var_2>=6.5e-06 then node 3 else 8847
DaveM@34 4968 2 class = 8847
DaveM@34 4969 3 class = 8827
DaveM@34 4970
DaveM@34 4971
DaveM@34 4972 row =
DaveM@34 4973
DaveM@34 4974 8190
DaveM@34 4975
DaveM@34 4976 Row: 8190, pDepth = 1, loss = 1.000000
DaveM@34 4977
DaveM@34 4978 Decision tree for classification
DaveM@34 4979 1 if beats_loudness_band_ratio_var_2<6.5e-06 then node 2 elseif beats_loudness_band_ratio_var_2>=6.5e-06 then node 3 else 8847
DaveM@34 4980 2 class = 8847
DaveM@34 4981 3 class = 8827
DaveM@34 4982
DaveM@34 4983
DaveM@34 4984 row =
DaveM@34 4985
DaveM@34 4986 8417
DaveM@34 4987
DaveM@34 4988 Row: 8417, pDepth = 1, loss = 1.000000
DaveM@34 4989
DaveM@34 4990 Decision tree for classification
DaveM@34 4991 1 if beats_loudness_band_ratio_var_2<6.5e-06 then node 2 elseif beats_loudness_band_ratio_var_2>=6.5e-06 then node 3 else 8847
DaveM@34 4992 2 class = 8847
DaveM@34 4993 3 class = 8827
DaveM@34 4994
DaveM@34 4995
DaveM@34 4996 row =
DaveM@34 4997
DaveM@34 4998 8433
DaveM@34 4999
DaveM@34 5000 Row: 8433, pDepth = 1, loss = 1.000000
DaveM@34 5001
DaveM@34 5002 Decision tree for classification
DaveM@34 5003 1 if beats_loudness_band_ratio_var_2<6.5e-06 then node 2 elseif beats_loudness_band_ratio_var_2>=6.5e-06 then node 3 else 8847
DaveM@34 5004 2 class = 8847
DaveM@34 5005 3 class = 8827
DaveM@34 5006
DaveM@34 5007
DaveM@34 5008 row =
DaveM@34 5009
DaveM@34 5010 8700
DaveM@34 5011
DaveM@34 5012 Row: 8700, pDepth = 1, loss = 1.000000
DaveM@34 5013
DaveM@34 5014 Decision tree for classification
DaveM@34 5015 1 if beats_loudness_band_ratio_var_2<6.5e-06 then node 2 elseif beats_loudness_band_ratio_var_2>=6.5e-06 then node 3 else 8847
DaveM@34 5016 2 class = 8847
DaveM@34 5017 3 class = 8827
DaveM@34 5018
DaveM@34 5019
DaveM@34 5020 row =
DaveM@34 5021
DaveM@34 5022 8610
DaveM@34 5023
DaveM@34 5024 Row: 8610, pDepth = 1, loss = 0.064516
DaveM@34 5025
DaveM@34 5026 Decision tree for classification
DaveM@34 5027 1 if frequency_bands_dvar_1<0.001967 then node 2 elseif frequency_bands_dvar_1>=0.001967 then node 3 else 8407
DaveM@34 5028 2 class = 7899
DaveM@34 5029 3 class = 8407
DaveM@34 5030
DaveM@34 5031
DaveM@34 5032 row =
DaveM@34 5033
DaveM@34 5034 8666
DaveM@34 5035
DaveM@34 5036 Row: 8666, pDepth = 1, loss = 0.047619
DaveM@34 5037
DaveM@34 5038 Decision tree for classification
DaveM@34 5039 1 if erb_bands_median_1<5e-07 then node 2 elseif erb_bands_median_1>=5e-07 then node 3 else 8314
DaveM@34 5040 2 class = 7766
DaveM@34 5041 3 class = 8314
DaveM@34 5042
DaveM@34 5043
DaveM@34 5044 row =
DaveM@34 5045
DaveM@34 5046 8089
DaveM@34 5047
DaveM@34 5048 Row: 8089, pDepth = 0, loss = 1.000000
DaveM@34 5049
DaveM@34 5050 Decision tree for classification
DaveM@34 5051 1 if erb_bands_median_1<5e-07 then node 2 elseif erb_bands_median_1>=5e-07 then node 3 else 8314
DaveM@34 5052 2 class = 7766
DaveM@34 5053 3 class = 8314
DaveM@34 5054
DaveM@34 5055
DaveM@34 5056 row =
DaveM@34 5057
DaveM@34 5058 8782
DaveM@34 5059
DaveM@34 5060 Row: 8782, pDepth = 1, loss = 0.062500
DaveM@34 5061
DaveM@34 5062 Decision tree for classification
DaveM@34 5063 1 if frequency_bands_dmean2_15<1.5e-06 then node 2 elseif frequency_bands_dmean2_15>=1.5e-06 then node 3 else 8556
DaveM@34 5064 2 class = 8695
DaveM@34 5065 3 class = 8556
DaveM@34 5066
DaveM@34 5067
DaveM@34 5068 row =
DaveM@34 5069
DaveM@34 5070 8709
DaveM@34 5071
DaveM@34 5072 Row: 8709, pDepth = 1, loss = 0.095238
DaveM@34 5073
DaveM@34 5074 Decision tree for classification
DaveM@34 5075 1 if first_peak_weight_min<0.816666 then node 2 elseif first_peak_weight_min>=0.816666 then node 3 else 8518
DaveM@34 5076 2 class = 8413
DaveM@34 5077 3 class = 8518
DaveM@34 5078
DaveM@34 5079
DaveM@34 5080 row =
DaveM@34 5081
DaveM@34 5082 8796
DaveM@34 5083
DaveM@34 5084 Row: 8796, pDepth = 3, loss = 0.172727
DaveM@34 5085
DaveM@34 5086 Decision tree for classification
DaveM@34 5087 1 if beats_loudness_band_ratio_mean_0<0.24911 then node 2 elseif beats_loudness_band_ratio_mean_0>=0.24911 then node 3 else 8756
DaveM@34 5088 2 class = 8756
DaveM@34 5089 3 class = 8636
DaveM@34 5090
DaveM@34 5091
DaveM@34 5092 row =
DaveM@34 5093
DaveM@34 5094 8810
DaveM@34 5095
DaveM@34 5096 Row: 8810, pDepth = 2, loss = 0.103448
DaveM@34 5097
DaveM@34 5098 Decision tree for classification
DaveM@34 5099 1 if beats_loudness_band_ratio_max_1<0.222984 then node 2 elseif beats_loudness_band_ratio_max_1>=0.222984 then node 3 else 8638
DaveM@34 5100 2 class = 8638
DaveM@34 5101 3 class = 8631
DaveM@34 5102
DaveM@34 5103
DaveM@34 5104 row =
DaveM@34 5105
DaveM@34 5106 8878
DaveM@34 5107
DaveM@34 5108 Row: 8878, pDepth = 1, loss = 0.044776
DaveM@34 5109
DaveM@34 5110 Decision tree for classification
DaveM@34 5111 1 if beats_loudness_band_ratio_median_3<5e-07 then node 2 elseif beats_loudness_band_ratio_median_3>=5e-07 then node 3 else 8855
DaveM@34 5112 2 class = 8732
DaveM@34 5113 3 class = 8855
DaveM@34 5114
DaveM@34 5115
DaveM@34 5116 row =
DaveM@34 5117
DaveM@34 5118 7450
DaveM@34 5119
DaveM@34 5120 Row: 7450, pDepth = 1, loss = 1.000000
DaveM@34 5121
DaveM@34 5122 Decision tree for classification
DaveM@34 5123 1 if beats_loudness_band_ratio_median_3<5e-07 then node 2 elseif beats_loudness_band_ratio_median_3>=5e-07 then node 3 else 8855
DaveM@34 5124 2 class = 8732
DaveM@34 5125 3 class = 8855
DaveM@34 5126
DaveM@34 5127
DaveM@34 5128 row =
DaveM@34 5129
DaveM@34 5130 8917
DaveM@34 5131
DaveM@34 5132 Row: 8917, pDepth = 1, loss = 1.000000
DaveM@34 5133
DaveM@34 5134 Decision tree for classification
DaveM@34 5135 1 if beats_loudness_band_ratio_median_3<5e-07 then node 2 elseif beats_loudness_band_ratio_median_3>=5e-07 then node 3 else 8855
DaveM@34 5136 2 class = 8732
DaveM@34 5137 3 class = 8855
DaveM@34 5138
DaveM@34 5139
DaveM@34 5140 row =
DaveM@34 5141
DaveM@34 5142 8590
DaveM@34 5143
DaveM@34 5144 Row: 8590, pDepth = 1, loss = 0.018868
DaveM@34 5145
DaveM@34 5146 Decision tree for classification
DaveM@34 5147 1 if beats_loudness_band_ratio_median_0<0.145259 then node 2 elseif beats_loudness_band_ratio_median_0>=0.145259 then node 3 else 7867
DaveM@34 5148 2 class = 7699
DaveM@34 5149 3 class = 7867
DaveM@34 5150
DaveM@34 5151
DaveM@34 5152 row =
DaveM@34 5153
DaveM@34 5154 8648
DaveM@34 5155
DaveM@34 5156 Row: 8648, pDepth = 1, loss = 1.000000
DaveM@34 5157
DaveM@34 5158 Decision tree for classification
DaveM@34 5159 1 if beats_loudness_band_ratio_median_0<0.145259 then node 2 elseif beats_loudness_band_ratio_median_0>=0.145259 then node 3 else 7867
DaveM@34 5160 2 class = 7699
DaveM@34 5161 3 class = 7867
DaveM@34 5162
DaveM@34 5163
DaveM@34 5164 row =
DaveM@34 5165
DaveM@34 5166 8596
DaveM@34 5167
DaveM@34 5168 Row: 8596, pDepth = 1, loss = 0.113208
DaveM@34 5169
DaveM@34 5170 Decision tree for classification
DaveM@34 5171 1 if logattacktime_max<0.590546 then node 2 elseif logattacktime_max>=0.590546 then node 3 else 8328
DaveM@34 5172 2 class = 8328
DaveM@34 5173 3 class = 8133
DaveM@34 5174
DaveM@34 5175
DaveM@34 5176 row =
DaveM@34 5177
DaveM@34 5178 8642
DaveM@34 5179
DaveM@34 5180 Row: 8642, pDepth = 1, loss = 0.049587
DaveM@34 5181
DaveM@34 5182 Decision tree for classification
DaveM@34 5183 1 if silence_rate_30dB_dvar2<0.015404 then node 2 elseif silence_rate_30dB_dvar2>=0.015404 then node 3 else 8343
DaveM@34 5184 2 class = 8343
DaveM@34 5185 3 class = 8559
DaveM@34 5186
DaveM@34 5187
DaveM@34 5188 row =
DaveM@34 5189
DaveM@34 5190 8673
DaveM@34 5191
DaveM@34 5192 Row: 8673, pDepth = 1, loss = 0.102041
DaveM@34 5193
DaveM@34 5194 Decision tree for classification
DaveM@34 5195 1 if scvalleys_max_1<0.593168 then node 2 elseif scvalleys_max_1>=0.593168 then node 3 else 8428
DaveM@34 5196 2 class = 8234
DaveM@34 5197 3 class = 8428
DaveM@34 5198
DaveM@34 5199
DaveM@34 5200 row =
DaveM@34 5201
DaveM@34 5202 8713
DaveM@34 5203
DaveM@34 5204 Row: 8713, pDepth = 1, loss = 1.000000
DaveM@34 5205
DaveM@34 5206 Decision tree for classification
DaveM@34 5207 1 if scvalleys_max_1<0.593168 then node 2 elseif scvalleys_max_1>=0.593168 then node 3 else 8428
DaveM@34 5208 2 class = 8234
DaveM@34 5209 3 class = 8428
DaveM@34 5210
DaveM@34 5211
DaveM@34 5212 row =
DaveM@34 5213
DaveM@34 5214 8742
DaveM@34 5215
DaveM@34 5216 Row: 8742, pDepth = 1, loss = 1.000000
DaveM@34 5217
DaveM@34 5218 Decision tree for classification
DaveM@34 5219 1 if scvalleys_max_1<0.593168 then node 2 elseif scvalleys_max_1>=0.593168 then node 3 else 8428
DaveM@34 5220 2 class = 8234
DaveM@34 5221 3 class = 8428
DaveM@34 5222
DaveM@34 5223
DaveM@34 5224 row =
DaveM@34 5225
DaveM@34 5226 8851
DaveM@34 5227
DaveM@34 5228 Row: 8851, pDepth = 5, loss = 0.162963
DaveM@34 5229
DaveM@34 5230 Decision tree for classification
DaveM@34 5231 1 if spectral_flatness_db_dvar<0.022638 then node 2 elseif spectral_flatness_db_dvar>=0.022638 then node 3 else 8717
DaveM@34 5232 2 class = 8580
DaveM@34 5233 3 class = 8717
DaveM@34 5234
DaveM@34 5235
DaveM@34 5236 row =
DaveM@34 5237
DaveM@34 5238 8458
DaveM@34 5239
DaveM@34 5240 Row: 8458, pDepth = 1, loss = 0.105263
DaveM@34 5241
DaveM@34 5242 Decision tree for classification
DaveM@34 5243 1 if spectral_entropy_var<0.008238 then node 2 elseif spectral_entropy_var>=0.008238 then node 3 else 8104
DaveM@34 5244 2 class = 7759
DaveM@34 5245 3 class = 8104
DaveM@34 5246
DaveM@34 5247
DaveM@34 5248 row =
DaveM@34 5249
DaveM@34 5250 8618
DaveM@34 5251
DaveM@34 5252 Row: 8618, pDepth = 2, loss = 0.109091
DaveM@34 5253
DaveM@34 5254 Decision tree for classification
DaveM@34 5255 1 if zerocrossingrate_max<0.495847 then node 2 elseif zerocrossingrate_max>=0.495847 then node 3 else 8545
DaveM@34 5256 2 class = 7886
DaveM@34 5257 3 class = 8545
DaveM@34 5258
DaveM@34 5259
DaveM@34 5260 row =
DaveM@34 5261
DaveM@34 5262 8817
DaveM@34 5263
DaveM@34 5264 Row: 8817, pDepth = 2, loss = 0.058824
DaveM@34 5265
DaveM@34 5266 Decision tree for classification
DaveM@34 5267 1 if scvalleys_min_1<0.063374 then node 2 elseif scvalleys_min_1>=0.063374 then node 3 else 8728
DaveM@34 5268 2 class = 8649
DaveM@34 5269 3 class = 8728
DaveM@34 5270
DaveM@34 5271
DaveM@34 5272 row =
DaveM@34 5273
DaveM@34 5274 8830
DaveM@34 5275
DaveM@34 5276 Row: 8830, pDepth = 4, loss = 0.154545
DaveM@34 5277
DaveM@34 5278 Decision tree for classification
DaveM@34 5279 1 if spectral_decrease_mean<0.89364 then node 2 elseif spectral_decrease_mean>=0.89364 then node 3 else 8674
DaveM@34 5280 2 class = 8646
DaveM@34 5281 3 class = 8674
DaveM@34 5282
DaveM@34 5283
DaveM@34 5284 row =
DaveM@34 5285
DaveM@34 5286 8521
DaveM@34 5287
DaveM@34 5288 Row: 8521, pDepth = 2, loss = 0.096154
DaveM@34 5289
DaveM@34 5290 Decision tree for classification
DaveM@34 5291 1 if beats_loudness_band_ratio_mean_0<0.644724 then node 2 elseif beats_loudness_band_ratio_mean_0>=0.644724 then node 3 else 8079
DaveM@34 5292 2 class = 7543
DaveM@34 5293 3 class = 8079
DaveM@34 5294
DaveM@34 5295
DaveM@34 5296 row =
DaveM@34 5297
DaveM@34 5298 8800
DaveM@34 5299
DaveM@34 5300 Row: 8800, pDepth = 1, loss = 1.000000
DaveM@34 5301
DaveM@34 5302 Decision tree for classification
DaveM@34 5303 1 if beats_loudness_band_ratio_mean_0<0.644724 then node 2 elseif beats_loudness_band_ratio_mean_0>=0.644724 then node 3 else 8079
DaveM@34 5304 2 class = 7543
DaveM@34 5305 3 class = 8079
DaveM@34 5306
DaveM@34 5307
DaveM@34 5308 row =
DaveM@34 5309
DaveM@34 5310 8845
DaveM@34 5311
DaveM@34 5312 Row: 8845, pDepth = 3, loss = 0.120253
DaveM@34 5313
DaveM@34 5314 Decision tree for classification
DaveM@34 5315 1 if spectral_energyband_high_max<0.001368 then node 2 elseif spectral_energyband_high_max>=0.001368 then node 3 else 8825
DaveM@34 5316 2 class = 8825
DaveM@34 5317 3 class = 8704
DaveM@34 5318
DaveM@34 5319
DaveM@34 5320 row =
DaveM@34 5321
DaveM@34 5322 8889
DaveM@34 5323
DaveM@34 5324 Row: 8889, pDepth = 1, loss = 0.027322
DaveM@34 5325
DaveM@34 5326 Decision tree for classification
DaveM@34 5327 1 if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
DaveM@34 5328 2 class = 8779
DaveM@34 5329 3 class = 8749
DaveM@34 5330
DaveM@34 5331
DaveM@34 5332 row =
DaveM@34 5333
DaveM@34 5334 6804
DaveM@34 5335
DaveM@34 5336 Row: 6804, pDepth = 0, loss = 1.000000
DaveM@34 5337
DaveM@34 5338 Decision tree for classification
DaveM@34 5339 1 if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
DaveM@34 5340 2 class = 8779
DaveM@34 5341 3 class = 8749
DaveM@34 5342
DaveM@34 5343
DaveM@34 5344 row =
DaveM@34 5345
DaveM@34 5346 7906
DaveM@34 5347
DaveM@34 5348 Row: 7906, pDepth = 1, loss = 1.000000
DaveM@34 5349
DaveM@34 5350 Decision tree for classification
DaveM@34 5351 1 if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
DaveM@34 5352 2 class = 8779
DaveM@34 5353 3 class = 8749
DaveM@34 5354
DaveM@34 5355
DaveM@34 5356 row =
DaveM@34 5357
DaveM@34 5358 6948
DaveM@34 5359
DaveM@34 5360 Row: 6948, pDepth = 0, loss = 1.000000
DaveM@34 5361
DaveM@34 5362 Decision tree for classification
DaveM@34 5363 1 if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
DaveM@34 5364 2 class = 8779
DaveM@34 5365 3 class = 8749
DaveM@34 5366
DaveM@34 5367
DaveM@34 5368 row =
DaveM@34 5369
DaveM@34 5370 8009
DaveM@34 5371
DaveM@34 5372 Row: 8009, pDepth = 1, loss = 1.000000
DaveM@34 5373
DaveM@34 5374 Decision tree for classification
DaveM@34 5375 1 if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
DaveM@34 5376 2 class = 8779
DaveM@34 5377 3 class = 8749
DaveM@34 5378
DaveM@34 5379
DaveM@34 5380 row =
DaveM@34 5381
DaveM@34 5382 7066
DaveM@34 5383
DaveM@34 5384 Row: 7066, pDepth = 1, loss = 1.000000
DaveM@34 5385
DaveM@34 5386 Decision tree for classification
DaveM@34 5387 1 if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
DaveM@34 5388 2 class = 8779
DaveM@34 5389 3 class = 8749
DaveM@34 5390
DaveM@34 5391
DaveM@34 5392 row =
DaveM@34 5393
DaveM@34 5394 7381
DaveM@34 5395
DaveM@34 5396 Row: 7381, pDepth = 1, loss = 1.000000
DaveM@34 5397
DaveM@34 5398 Decision tree for classification
DaveM@34 5399 1 if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
DaveM@34 5400 2 class = 8779
DaveM@34 5401 3 class = 8749
DaveM@34 5402
DaveM@34 5403
DaveM@34 5404 row =
DaveM@34 5405
DaveM@34 5406 6675
DaveM@34 5407
DaveM@34 5408 Row: 6675, pDepth = 1, loss = 1.000000
DaveM@34 5409
DaveM@34 5410 Decision tree for classification
DaveM@34 5411 1 if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
DaveM@34 5412 2 class = 8779
DaveM@34 5413 3 class = 8749
DaveM@34 5414
DaveM@34 5415
DaveM@34 5416 row =
DaveM@34 5417
DaveM@34 5418 7995
DaveM@34 5419
DaveM@34 5420 Row: 7995, pDepth = 1, loss = 0.047619
DaveM@34 5421
DaveM@34 5422 Decision tree for classification
DaveM@34 5423 1 if spectral_flux_dmean<0.007513 then node 2 elseif spectral_flux_dmean>=0.007513 then node 3 else 7083
DaveM@34 5424 2 class = 7083
DaveM@34 5425 3 class = 7681
DaveM@34 5426
DaveM@34 5427
DaveM@34 5428 row =
DaveM@34 5429
DaveM@34 5430 8669
DaveM@34 5431
DaveM@34 5432 Row: 8669, pDepth = 1, loss = 1.000000
DaveM@34 5433
DaveM@34 5434 Decision tree for classification
DaveM@34 5435 1 if spectral_flux_dmean<0.007513 then node 2 elseif spectral_flux_dmean>=0.007513 then node 3 else 7083
DaveM@34 5436 2 class = 7083
DaveM@34 5437 3 class = 7681
DaveM@34 5438
DaveM@34 5439
DaveM@34 5440 row =
DaveM@34 5441
DaveM@34 5442 8820
DaveM@34 5443
DaveM@34 5444 Row: 8820, pDepth = 2, loss = 0.169014
DaveM@34 5445
DaveM@34 5446 Decision tree for classification
DaveM@34 5447 1 if effective_duration_min<0.103585 then node 2 elseif effective_duration_min>=0.103585 then node 3 else 8797
DaveM@34 5448 2 class = 8680
DaveM@34 5449 3 class = 8797
DaveM@34 5450
DaveM@34 5451
DaveM@34 5452 row =
DaveM@34 5453
DaveM@34 5454 8795
DaveM@34 5455
DaveM@34 5456 Row: 8795, pDepth = 2, loss = 0.100000
DaveM@34 5457
DaveM@34 5458 Decision tree for classification
DaveM@34 5459 1 if spectral_energy_var<0.0035125 then node 2 elseif spectral_energy_var>=0.0035125 then node 3 else 8762
DaveM@34 5460 2 class = 8762
DaveM@34 5461 3 class = 8558
DaveM@34 5462
DaveM@34 5463
DaveM@34 5464 row =
DaveM@34 5465
DaveM@34 5466 8859
DaveM@34 5467
DaveM@34 5468 Row: 8859, pDepth = 1, loss = 1.000000
DaveM@34 5469
DaveM@34 5470 Decision tree for classification
DaveM@34 5471 1 if spectral_energy_var<0.0035125 then node 2 elseif spectral_energy_var>=0.0035125 then node 3 else 8762
DaveM@34 5472 2 class = 8762
DaveM@34 5473 3 class = 8558
DaveM@34 5474
DaveM@34 5475
DaveM@34 5476 row =
DaveM@34 5477
DaveM@34 5478 8834
DaveM@34 5479
DaveM@34 5480 Row: 8834, pDepth = 1, loss = 1.000000
DaveM@34 5481
DaveM@34 5482 Decision tree for classification
DaveM@34 5483 1 if spectral_energy_var<0.0035125 then node 2 elseif spectral_energy_var>=0.0035125 then node 3 else 8762
DaveM@34 5484 2 class = 8762
DaveM@34 5485 3 class = 8558
DaveM@34 5486
DaveM@34 5487
DaveM@34 5488 row =
DaveM@34 5489
DaveM@34 5490 8844
DaveM@34 5491
DaveM@34 5492 Row: 8844, pDepth = 3, loss = 0.180180
DaveM@34 5493
DaveM@34 5494 Decision tree for classification
DaveM@34 5495 1 if first_peak_spread_min<0.069737 then node 2 elseif first_peak_spread_min>=0.069737 then node 3 else 8676
DaveM@34 5496 2 class = 8676
DaveM@34 5497 3 class = 8647
DaveM@34 5498
DaveM@34 5499
DaveM@34 5500 row =
DaveM@34 5501
DaveM@34 5502 8849
DaveM@34 5503
DaveM@34 5504 Row: 8849, pDepth = 1, loss = 1.000000
DaveM@34 5505
DaveM@34 5506 Decision tree for classification
DaveM@34 5507 1 if first_peak_spread_min<0.069737 then node 2 elseif first_peak_spread_min>=0.069737 then node 3 else 8676
DaveM@34 5508 2 class = 8676
DaveM@34 5509 3 class = 8647
DaveM@34 5510
DaveM@34 5511
DaveM@34 5512 row =
DaveM@34 5513
DaveM@34 5514 8853
DaveM@34 5515
DaveM@34 5516 Row: 8853, pDepth = 1, loss = 0.060000
DaveM@34 5517
DaveM@34 5518 Decision tree for classification
DaveM@34 5519 1 if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
DaveM@34 5520 2 class = 8597
DaveM@34 5521 3 class = 8747
DaveM@34 5522
DaveM@34 5523
DaveM@34 5524 row =
DaveM@34 5525
DaveM@34 5526 6761
DaveM@34 5527
DaveM@34 5528 Row: 6761, pDepth = 0, loss = 1.000000
DaveM@34 5529
DaveM@34 5530 Decision tree for classification
DaveM@34 5531 1 if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
DaveM@34 5532 2 class = 8597
DaveM@34 5533 3 class = 8747
DaveM@34 5534
DaveM@34 5535
DaveM@34 5536 row =
DaveM@34 5537
DaveM@34 5538 6868
DaveM@34 5539
DaveM@34 5540 Row: 6868, pDepth = 0, loss = 1.000000
DaveM@34 5541
DaveM@34 5542 Decision tree for classification
DaveM@34 5543 1 if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
DaveM@34 5544 2 class = 8597
DaveM@34 5545 3 class = 8747
DaveM@34 5546
DaveM@34 5547
DaveM@34 5548 row =
DaveM@34 5549
DaveM@34 5550 7824
DaveM@34 5551
DaveM@34 5552 Row: 7824, pDepth = 1, loss = 1.000000
DaveM@34 5553
DaveM@34 5554 Decision tree for classification
DaveM@34 5555 1 if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
DaveM@34 5556 2 class = 8597
DaveM@34 5557 3 class = 8747
DaveM@34 5558
DaveM@34 5559
DaveM@34 5560 row =
DaveM@34 5561
DaveM@34 5562 6110
DaveM@34 5563
DaveM@34 5564 Row: 6110, pDepth = 0, loss = 1.000000
DaveM@34 5565
DaveM@34 5566 Decision tree for classification
DaveM@34 5567 1 if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
DaveM@34 5568 2 class = 8597
DaveM@34 5569 3 class = 8747
DaveM@34 5570
DaveM@34 5571
DaveM@34 5572 row =
DaveM@34 5573
DaveM@34 5574 7223
DaveM@34 5575
DaveM@34 5576 Row: 7223, pDepth = 0, loss = 1.000000
DaveM@34 5577
DaveM@34 5578 Decision tree for classification
DaveM@34 5579 1 if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
DaveM@34 5580 2 class = 8597
DaveM@34 5581 3 class = 8747
DaveM@34 5582
DaveM@34 5583
DaveM@34 5584 row =
DaveM@34 5585
DaveM@34 5586 8172
DaveM@34 5587
DaveM@34 5588 Row: 8172, pDepth = 1, loss = 1.000000
DaveM@34 5589
DaveM@34 5590 Decision tree for classification
DaveM@34 5591 1 if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
DaveM@34 5592 2 class = 8597
DaveM@34 5593 3 class = 8747
DaveM@34 5594
DaveM@34 5595
DaveM@34 5596 row =
DaveM@34 5597
DaveM@34 5598 8539
DaveM@34 5599
DaveM@34 5600 Row: 8539, pDepth = 1, loss = 0.027778
DaveM@34 5601
DaveM@34 5602 Decision tree for classification
DaveM@34 5603 1 if beats_loudness_band_ratio_var_5<0.0411295 then node 2 elseif beats_loudness_band_ratio_var_5>=0.0411295 then node 3 else 8388
DaveM@34 5604 2 class = 7930
DaveM@34 5605 3 class = 8388
DaveM@34 5606
DaveM@34 5607
DaveM@34 5608 row =
DaveM@34 5609
DaveM@34 5610 4617
DaveM@34 5611
DaveM@34 5612 Row: 4617, pDepth = 0, loss = 1.000000
DaveM@34 5613
DaveM@34 5614 Decision tree for classification
DaveM@34 5615 1 if beats_loudness_band_ratio_var_5<0.0411295 then node 2 elseif beats_loudness_band_ratio_var_5>=0.0411295 then node 3 else 8388
DaveM@34 5616 2 class = 7930
DaveM@34 5617 3 class = 8388
DaveM@34 5618
DaveM@34 5619
DaveM@34 5620 row =
DaveM@34 5621
DaveM@34 5622 7161
DaveM@34 5623
DaveM@34 5624 Row: 7161, pDepth = 0, loss = 1.000000
DaveM@34 5625
DaveM@34 5626 Decision tree for classification
DaveM@34 5627 1 if beats_loudness_band_ratio_var_5<0.0411295 then node 2 elseif beats_loudness_band_ratio_var_5>=0.0411295 then node 3 else 8388
DaveM@34 5628 2 class = 7930
DaveM@34 5629 3 class = 8388
DaveM@34 5630
DaveM@34 5631
DaveM@34 5632 row =
DaveM@34 5633
DaveM@34 5634 8438
DaveM@34 5635
DaveM@34 5636 Row: 8438, pDepth = 0, loss = 1.000000
DaveM@34 5637
DaveM@34 5638 Decision tree for classification
DaveM@34 5639 1 if beats_loudness_band_ratio_var_5<0.0411295 then node 2 elseif beats_loudness_band_ratio_var_5>=0.0411295 then node 3 else 8388
DaveM@34 5640 2 class = 7930
DaveM@34 5641 3 class = 8388
DaveM@34 5642
DaveM@34 5643
DaveM@34 5644 row =
DaveM@34 5645
DaveM@34 5646 8526
DaveM@34 5647
DaveM@34 5648 Row: 8526, pDepth = 0, loss = 1.000000
DaveM@34 5649
DaveM@34 5650 Decision tree for classification
DaveM@34 5651 1 if beats_loudness_band_ratio_var_5<0.0411295 then node 2 elseif beats_loudness_band_ratio_var_5>=0.0411295 then node 3 else 8388
DaveM@34 5652 2 class = 7930
DaveM@34 5653 3 class = 8388
DaveM@34 5654
DaveM@34 5655
DaveM@34 5656 row =
DaveM@34 5657
DaveM@34 5658 8486
DaveM@34 5659
DaveM@34 5660 Row: 8486, pDepth = 0, loss = 1.000000
DaveM@34 5661
DaveM@34 5662 Decision tree for classification
DaveM@34 5663 1 if beats_loudness_band_ratio_var_5<0.0411295 then node 2 elseif beats_loudness_band_ratio_var_5>=0.0411295 then node 3 else 8388
DaveM@34 5664 2 class = 7930
DaveM@34 5665 3 class = 8388
DaveM@34 5666
DaveM@34 5667
DaveM@34 5668 row =
DaveM@34 5669
DaveM@34 5670 8729
DaveM@34 5671
DaveM@34 5672 Row: 8729, pDepth = 0, loss = 1.000000
DaveM@34 5673
DaveM@34 5674 Decision tree for classification
DaveM@34 5675 1 if beats_loudness_band_ratio_var_5<0.0411295 then node 2 elseif beats_loudness_band_ratio_var_5>=0.0411295 then node 3 else 8388
DaveM@34 5676 2 class = 7930
DaveM@34 5677 3 class = 8388
DaveM@34 5678
DaveM@34 5679
DaveM@34 5680 row =
DaveM@34 5681
DaveM@34 5682 8687
DaveM@34 5683
DaveM@34 5684 Row: 8687, pDepth = 1, loss = 1.000000
DaveM@34 5685
DaveM@34 5686 Decision tree for classification
DaveM@34 5687 1 if beats_loudness_band_ratio_var_5<0.0411295 then node 2 elseif beats_loudness_band_ratio_var_5>=0.0411295 then node 3 else 8388
DaveM@34 5688 2 class = 7930
DaveM@34 5689 3 class = 8388
DaveM@34 5690
DaveM@34 5691
DaveM@34 5692 row =
DaveM@34 5693
DaveM@34 5694 8806
DaveM@34 5695
DaveM@34 5696 Row: 8806, pDepth = 1, loss = 0.082353
DaveM@34 5697
DaveM@34 5698 Decision tree for classification
DaveM@34 5699 1 if silence_rate_20dB_dmean<0.017467 then node 2 elseif silence_rate_20dB_dmean>=0.017467 then node 3 else 8668
DaveM@34 5700 2 class = 8668
DaveM@34 5701 3 class = 8609
DaveM@34 5702
DaveM@34 5703
DaveM@34 5704 row =
DaveM@34 5705
DaveM@34 5706 8600
DaveM@34 5707
DaveM@34 5708 Row: 8600, pDepth = 0, loss = 1.000000
DaveM@34 5709
DaveM@34 5710 Decision tree for classification
DaveM@34 5711 1 if silence_rate_20dB_dmean<0.017467 then node 2 elseif silence_rate_20dB_dmean>=0.017467 then node 3 else 8668
DaveM@34 5712 2 class = 8668
DaveM@34 5713 3 class = 8609
DaveM@34 5714
DaveM@34 5715
DaveM@34 5716 row =
DaveM@34 5717
DaveM@34 5718 8866
DaveM@34 5719
DaveM@34 5720 Row: 8866, pDepth = 2, loss = 0.150000
DaveM@34 5721
DaveM@34 5722 Decision tree for classification
DaveM@34 5723 1 if barkbands_var_11<0.000645 then node 2 elseif barkbands_var_11>=0.000645 then node 3 else 8809
DaveM@34 5724 2 class = 8809
DaveM@34 5725 3 if frequency_bands_max_8<0.0328895 then node 4 elseif frequency_bands_max_8>=0.0328895 then node 5 else 8839
DaveM@34 5726 4 class = 8839
DaveM@34 5727 5 if barkbands_max_21<1.9e-05 then node 6 elseif barkbands_max_21>=1.9e-05 then node 7 else 8809
DaveM@34 5728 6 class = 8839
DaveM@34 5729 7 class = 8809
DaveM@34 5730
DaveM@34 5731
DaveM@34 5732 row =
DaveM@34 5733
DaveM@34 5734 7749
DaveM@34 5735
DaveM@34 5736 Row: 7749, pDepth = 1, loss = 1.000000
DaveM@34 5737
DaveM@34 5738 Decision tree for classification
DaveM@34 5739 1 if barkbands_var_11<0.000645 then node 2 elseif barkbands_var_11>=0.000645 then node 3 else 8809
DaveM@34 5740 2 class = 8809
DaveM@34 5741 3 if frequency_bands_max_8<0.0328895 then node 4 elseif frequency_bands_max_8>=0.0328895 then node 5 else 8839
DaveM@34 5742 4 class = 8839
DaveM@34 5743 5 if barkbands_max_21<1.9e-05 then node 6 elseif barkbands_max_21>=1.9e-05 then node 7 else 8809
DaveM@34 5744 6 class = 8839
DaveM@34 5745 7 class = 8809
DaveM@34 5746
DaveM@34 5747
DaveM@34 5748 row =
DaveM@34 5749
DaveM@34 5750 8736
DaveM@34 5751
DaveM@34 5752 Row: 8736, pDepth = 1, loss = 0.056604
DaveM@34 5753
DaveM@34 5754 Decision tree for classification
DaveM@34 5755 1 if beats_loudness_band_ratio_mean_5<0.391387 then node 2 elseif beats_loudness_band_ratio_mean_5>=0.391387 then node 3 else 8691
DaveM@34 5756 2 class = 8691
DaveM@34 5757 3 class = 8416
DaveM@34 5758
DaveM@34 5759
DaveM@34 5760 row =
DaveM@34 5761
DaveM@34 5762 8805
DaveM@34 5763
DaveM@34 5764 Row: 8805, pDepth = 1, loss = 0.057971
DaveM@34 5765
DaveM@34 5766 Decision tree for classification
DaveM@34 5767 1 if spectral_contrast_max_1<0.574351 then node 2 elseif spectral_contrast_max_1>=0.574351 then node 3 else 8684
DaveM@34 5768 2 class = 8550
DaveM@34 5769 3 class = 8684
DaveM@34 5770
DaveM@34 5771
DaveM@34 5772 row =
DaveM@34 5773
DaveM@34 5774 8874
DaveM@34 5775
DaveM@34 5776 Row: 8874, pDepth = 4, loss = 0.122807
DaveM@34 5777
DaveM@34 5778 Decision tree for classification
DaveM@34 5779 1 if beats_loudness_band_ratio_max_3<0.0886285 then node 2 elseif beats_loudness_band_ratio_max_3>=0.0886285 then node 3 else 8770
DaveM@34 5780 2 class = 8798
DaveM@34 5781 3 class = 8770
DaveM@34 5782
DaveM@34 5783
DaveM@34 5784 row =
DaveM@34 5785
DaveM@34 5786 8654
DaveM@34 5787
DaveM@34 5788 Row: 8654, pDepth = 1, loss = 1.000000
DaveM@34 5789
DaveM@34 5790 Decision tree for classification
DaveM@34 5791 1 if beats_loudness_band_ratio_max_3<0.0886285 then node 2 elseif beats_loudness_band_ratio_max_3>=0.0886285 then node 3 else 8770
DaveM@34 5792 2 class = 8798
DaveM@34 5793 3 class = 8770
DaveM@34 5794
DaveM@34 5795
DaveM@34 5796 row =
DaveM@34 5797
DaveM@34 5798 8706
DaveM@34 5799
DaveM@34 5800 Row: 8706, pDepth = 1, loss = 0.044444
DaveM@34 5801
DaveM@34 5802 Decision tree for classification
DaveM@34 5803 1 if spectral_contrast_max_1<0.641064 then node 2 elseif spectral_contrast_max_1>=0.641064 then node 3 else 8504
DaveM@34 5804 2 class = 8504
DaveM@34 5805 3 class = 8192
DaveM@34 5806
DaveM@34 5807
DaveM@34 5808 row =
DaveM@34 5809
DaveM@34 5810 8822
DaveM@34 5811
DaveM@34 5812 Row: 8822, pDepth = 1, loss = 0.063291
DaveM@34 5813
DaveM@34 5814 Decision tree for classification
DaveM@34 5815 1 if inharmonicity_mean<0.0065225 then node 2 elseif inharmonicity_mean>=0.0065225 then node 3 else 8763
DaveM@34 5816 2 class = 8651
DaveM@34 5817 3 class = 8763
DaveM@34 5818
DaveM@34 5819
DaveM@34 5820 row =
DaveM@34 5821
DaveM@34 5822 8887
DaveM@34 5823
DaveM@34 5824 Row: 8887, pDepth = 6, loss = 0.137566
DaveM@34 5825
DaveM@34 5826 Decision tree for classification
DaveM@34 5827 1 if spectral_centroid_var<0.0022275 then node 2 elseif spectral_centroid_var>=0.0022275 then node 3 else 8818
DaveM@34 5828 2 class = 8685
DaveM@34 5829 3 class = 8818
DaveM@34 5830
DaveM@34 5831
DaveM@34 5832 row =
DaveM@34 5833
DaveM@34 5834 8593
DaveM@34 5835
DaveM@34 5836 Row: 8593, pDepth = 1, loss = 1.000000
DaveM@34 5837
DaveM@34 5838 Decision tree for classification
DaveM@34 5839 1 if spectral_centroid_var<0.0022275 then node 2 elseif spectral_centroid_var>=0.0022275 then node 3 else 8818
DaveM@34 5840 2 class = 8685
DaveM@34 5841 3 class = 8818
DaveM@34 5842
DaveM@34 5843
DaveM@34 5844 row =
DaveM@34 5845
DaveM@34 5846 8793
DaveM@34 5847
DaveM@34 5848 Row: 8793, pDepth = 1, loss = 1.000000
DaveM@34 5849
DaveM@34 5850 Decision tree for classification
DaveM@34 5851 1 if spectral_centroid_var<0.0022275 then node 2 elseif spectral_centroid_var>=0.0022275 then node 3 else 8818
DaveM@34 5852 2 class = 8685
DaveM@34 5853 3 class = 8818
DaveM@34 5854
DaveM@34 5855
DaveM@34 5856 row =
DaveM@34 5857
DaveM@34 5858 8867
DaveM@34 5859
DaveM@34 5860 Row: 8867, pDepth = 1, loss = 0.047619
DaveM@34 5861
DaveM@34 5862 Decision tree for classification
DaveM@34 5863 1 if gfcc_mean_0<0.826244 then node 2 elseif gfcc_mean_0>=0.826244 then node 3 else 8751
DaveM@34 5864 2 class = 8751
DaveM@34 5865 3 class = 8814
DaveM@34 5866
DaveM@34 5867
DaveM@34 5868 row =
DaveM@34 5869
DaveM@34 5870 8907
DaveM@34 5871
DaveM@34 5872 Row: 8907, pDepth = 3, loss = 0.106280
DaveM@34 5873
DaveM@34 5874 Decision tree for classification
DaveM@34 5875 1 if spectral_contrast_max_4<0.46882 then node 2 elseif spectral_contrast_max_4>=0.46882 then node 3 else 8892
DaveM@34 5876 2 class = 8892
DaveM@34 5877 3 class = 8787
DaveM@34 5878
DaveM@34 5879
DaveM@34 5880 row =
DaveM@34 5881
DaveM@34 5882 7000
DaveM@34 5883
DaveM@34 5884
DaveM@34 5885 row =
DaveM@34 5886
DaveM@34 5887 6920
DaveM@34 5888
DaveM@34 5889
DaveM@34 5890 row =
DaveM@34 5891
DaveM@34 5892 7265
DaveM@34 5893
DaveM@34 5894
DaveM@34 5895 row =
DaveM@34 5896
DaveM@34 5897 7536
DaveM@34 5898
DaveM@34 5899
DaveM@34 5900 row =
DaveM@34 5901
DaveM@34 5902 6798
DaveM@34 5903
DaveM@34 5904
DaveM@34 5905 row =
DaveM@34 5906
DaveM@34 5907 7374
DaveM@34 5908
DaveM@34 5909
DaveM@34 5910 row =
DaveM@34 5911
DaveM@34 5912 4902
DaveM@34 5913
DaveM@34 5914
DaveM@34 5915 row =
DaveM@34 5916
DaveM@34 5917 6179
DaveM@34 5918
DaveM@34 5919
DaveM@34 5920 row =
DaveM@34 5921
DaveM@34 5922 8193
DaveM@34 5923
DaveM@34 5924
DaveM@34 5925 row =
DaveM@34 5926
DaveM@34 5927 7938
DaveM@34 5928
DaveM@34 5929
DaveM@34 5930 row =
DaveM@34 5931
DaveM@34 5932 8055
DaveM@34 5933
DaveM@34 5934
DaveM@34 5935 row =
DaveM@34 5936
DaveM@34 5937 8023
DaveM@34 5938
DaveM@34 5939
DaveM@34 5940 row =
DaveM@34 5941
DaveM@34 5942 8281
DaveM@34 5943
DaveM@34 5944
DaveM@34 5945 row =
DaveM@34 5946
DaveM@34 5947 6958
DaveM@34 5948
DaveM@34 5949
DaveM@34 5950 row =
DaveM@34 5951
DaveM@34 5952 7244
DaveM@34 5953
DaveM@34 5954
DaveM@34 5955 row =
DaveM@34 5956
DaveM@34 5957 7873
DaveM@34 5958
DaveM@34 5959
DaveM@34 5960 row =
DaveM@34 5961
DaveM@34 5962 7796
DaveM@34 5963
DaveM@34 5964
DaveM@34 5965 row =
DaveM@34 5966
DaveM@34 5967 8034
DaveM@34 5968
DaveM@34 5969
DaveM@34 5970 row =
DaveM@34 5971
DaveM@34 5972 8350
DaveM@34 5973
DaveM@34 5974
DaveM@34 5975 row =
DaveM@34 5976
DaveM@34 5977 7936
DaveM@34 5978
DaveM@34 5979
DaveM@34 5980 row =
DaveM@34 5981
DaveM@34 5982 8411
DaveM@34 5983
DaveM@34 5984
DaveM@34 5985 row =
DaveM@34 5986
DaveM@34 5987 5805
DaveM@34 5988
DaveM@34 5989
DaveM@34 5990 row =
DaveM@34 5991
DaveM@34 5992 6364
DaveM@34 5993
DaveM@34 5994
DaveM@34 5995 row =
DaveM@34 5996
DaveM@34 5997 6233
DaveM@34 5998
DaveM@34 5999
DaveM@34 6000 row =
DaveM@34 6001
DaveM@34 6002 7341
DaveM@34 6003
DaveM@34 6004
DaveM@34 6005 row =
DaveM@34 6006
DaveM@34 6007 8080
DaveM@34 6008
DaveM@34 6009
DaveM@34 6010 row =
DaveM@34 6011
DaveM@34 6012 1987
DaveM@34 6013
DaveM@34 6014
DaveM@34 6015 row =
DaveM@34 6016
DaveM@34 6017 6722
DaveM@34 6018
DaveM@34 6019
DaveM@34 6020 row =
DaveM@34 6021
DaveM@34 6022 7116
DaveM@34 6023
DaveM@34 6024
DaveM@34 6025 row =
DaveM@34 6026
DaveM@34 6027 7388
DaveM@34 6028
DaveM@34 6029
DaveM@34 6030 row =
DaveM@34 6031
DaveM@34 6032 7674
DaveM@34 6033
DaveM@34 6034
DaveM@34 6035 row =
DaveM@34 6036
DaveM@34 6037 6367
DaveM@34 6038
DaveM@34 6039
DaveM@34 6040 row =
DaveM@34 6041
DaveM@34 6042 8135
DaveM@34 6043
DaveM@34 6044
DaveM@34 6045 row =
DaveM@34 6046
DaveM@34 6047 7974
DaveM@34 6048
DaveM@34 6049
DaveM@34 6050 row =
DaveM@34 6051
DaveM@34 6052 8356
DaveM@34 6053
DaveM@34 6054
DaveM@34 6055 row =
DaveM@34 6056
DaveM@34 6057 7961
DaveM@34 6058
DaveM@34 6059
DaveM@34 6060 row =
DaveM@34 6061
DaveM@34 6062 8607
DaveM@34 6063
DaveM@34 6064
DaveM@34 6065 row =
DaveM@34 6066
DaveM@34 6067 8571
DaveM@34 6068
DaveM@34 6069
DaveM@34 6070 row =
DaveM@34 6071
DaveM@34 6072 8639
DaveM@34 6073
DaveM@34 6074
DaveM@34 6075 row =
DaveM@34 6076
DaveM@34 6077 8505
DaveM@34 6078
DaveM@34 6079
DaveM@34 6080 row =
DaveM@34 6081
DaveM@34 6082 8766
DaveM@34 6083
DaveM@34 6084
DaveM@34 6085 row =
DaveM@34 6086
DaveM@34 6087 8663
DaveM@34 6088
DaveM@34 6089
DaveM@34 6090 row =
DaveM@34 6091
DaveM@34 6092 8705
DaveM@34 6093
DaveM@34 6094
DaveM@34 6095 row =
DaveM@34 6096
DaveM@34 6097 7946
DaveM@34 6098
DaveM@34 6099
DaveM@34 6100 row =
DaveM@34 6101
DaveM@34 6102 8228
DaveM@34 6103
DaveM@34 6104
DaveM@34 6105 row =
DaveM@34 6106
DaveM@34 6107 7558
DaveM@34 6108
DaveM@34 6109
DaveM@34 6110 row =
DaveM@34 6111
DaveM@34 6112 8186
DaveM@34 6113
DaveM@34 6114
DaveM@34 6115 row =
DaveM@34 6116
DaveM@34 6117 5006
DaveM@34 6118
DaveM@34 6119
DaveM@34 6120 row =
DaveM@34 6121
DaveM@34 6122 6126
DaveM@34 6123
DaveM@34 6124
DaveM@34 6125 row =
DaveM@34 6126
DaveM@34 6127 6974
DaveM@34 6128
DaveM@34 6129
DaveM@34 6130 row =
DaveM@34 6131
DaveM@34 6132 8352
DaveM@34 6133
DaveM@34 6134
DaveM@34 6135 row =
DaveM@34 6136
DaveM@34 6137 8303
DaveM@34 6138
DaveM@34 6139
DaveM@34 6140 row =
DaveM@34 6141
DaveM@34 6142 8643
DaveM@34 6143
DaveM@34 6144
DaveM@34 6145 row =
DaveM@34 6146
DaveM@34 6147 8620
DaveM@34 6148
DaveM@34 6149
DaveM@34 6150 row =
DaveM@34 6151
DaveM@34 6152 8778
DaveM@34 6153
DaveM@34 6154
DaveM@34 6155 row =
DaveM@34 6156
DaveM@34 6157 8576
DaveM@34 6158
DaveM@34 6159
DaveM@34 6160 row =
DaveM@34 6161
DaveM@34 6162 8591
DaveM@34 6163
DaveM@34 6164
DaveM@34 6165 row =
DaveM@34 6166
DaveM@34 6167 8625
DaveM@34 6168
DaveM@34 6169
DaveM@34 6170 row =
DaveM@34 6171
DaveM@34 6172 8733
DaveM@34 6173
DaveM@34 6174
DaveM@34 6175 row =
DaveM@34 6176
DaveM@34 6177 7703
DaveM@34 6178
DaveM@34 6179
DaveM@34 6180 row =
DaveM@34 6181
DaveM@34 6182 8436
DaveM@34 6183
DaveM@34 6184
DaveM@34 6185 row =
DaveM@34 6186
DaveM@34 6187 8452
DaveM@34 6188
DaveM@34 6189
DaveM@34 6190 row =
DaveM@34 6191
DaveM@34 6192 8664
DaveM@34 6193
DaveM@34 6194
DaveM@34 6195 row =
DaveM@34 6196
DaveM@34 6197 8091
DaveM@34 6198
DaveM@34 6199
DaveM@34 6200 row =
DaveM@34 6201
DaveM@34 6202 8219
DaveM@34 6203
DaveM@34 6204
DaveM@34 6205 row =
DaveM@34 6206
DaveM@34 6207 8522
DaveM@34 6208
DaveM@34 6209
DaveM@34 6210 row =
DaveM@34 6211
DaveM@34 6212 8547
DaveM@34 6213
DaveM@34 6214
DaveM@34 6215 row =
DaveM@34 6216
DaveM@34 6217 8511
DaveM@34 6218
DaveM@34 6219
DaveM@34 6220 row =
DaveM@34 6221
DaveM@34 6222 8575
DaveM@34 6223
DaveM@34 6224
DaveM@34 6225 row =
DaveM@34 6226
DaveM@34 6227 8524
DaveM@34 6228
DaveM@34 6229
DaveM@34 6230 row =
DaveM@34 6231
DaveM@34 6232 8326
DaveM@34 6233
DaveM@34 6234
DaveM@34 6235 row =
DaveM@34 6236
DaveM@34 6237 8561
DaveM@34 6238
DaveM@34 6239
DaveM@34 6240 row =
DaveM@34 6241
DaveM@34 6242 8439
DaveM@34 6243
DaveM@34 6244
DaveM@34 6245 row =
DaveM@34 6246
DaveM@34 6247 8461
DaveM@34 6248
DaveM@34 6249
DaveM@34 6250 row =
DaveM@34 6251
DaveM@34 6252 2361
DaveM@34 6253
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