view analysis/AnalysisOutput.txt @ 37:d9a9a6b93026 tip

Add README
author DaveM
date Sat, 01 Apr 2017 17:03:14 +0100
parents 6155f4e3d37c
children
line wrap: on
line source
save('AdobeAllResults.mat')
analysisWorkflow

row =

        8976

Row: 8976, pDepth = 2, loss = 0.002673

Decision tree for classification
1  if first_peak_weight_mean<0.0357145 then node 2 elseif first_peak_weight_mean>=0.0357145 then node 3 else 8975
2  class = 8974
3  class = 8975


row =

        8974

Row: 8974, pDepth = 20, loss = 0.079383

Decision tree for classification
1  if silence_rate_60dB_mean<0.495098 then node 2 elseif silence_rate_60dB_mean>=0.495098 then node 3 else 8966
2  class = 8963
3  class = 8966


row =

        8975

Row: 8975, pDepth = 37, loss = 0.153664

Decision tree for classification
1  if silence_rate_60dB_mean<0.47305 then node 2 elseif silence_rate_60dB_mean>=0.47305 then node 3 else 8972
2  class = 8973
3  class = 8972


row =

        8963

Row: 8963, pDepth = 11, loss = 0.102637

Decision tree for classification
1  if spectral_decrease_mean<0.866593 then node 2 elseif spectral_decrease_mean>=0.866593 then node 3 else 8959
2  class = 8930
3  class = 8959


row =

        8966

Row: 8966, pDepth = 16, loss = 0.129073

Decision tree for classification
1  if spectral_centroid_max<0.413299 then node 2 elseif spectral_centroid_max>=0.413299 then node 3 else 8956
2  class = 8928
3  class = 8956


row =

        8972

Row: 8972, pDepth = 15, loss = 0.112521

Decision tree for classification
1  if second_peak_bpm_max<0.262195 then node 2 elseif second_peak_bpm_max>=0.262195 then node 3 else 8971
2  class = 8971
3  class = 8969


row =

        8973

Row: 8973, pDepth = 16, loss = 0.152279

Decision tree for classification
1  if second_peak_weight_min<0.0616035 then node 2 elseif second_peak_weight_min>=0.0616035 then node 3 else 8970
2  class = 8970
3  class = 8967


row =

        8930

Row: 8930, pDepth = 3, loss = 0.135417

Decision tree for classification
1  if scvalleys_min_3<0.509141 then node 2 elseif scvalleys_min_3>=0.509141 then node 3 else 8879
2  class = 8879
3  class = 8863


row =

        8959

Row: 8959, pDepth = 11, loss = 0.145977

Decision tree for classification
1  if spectral_flatness_db_mean<0.271369 then node 2 elseif spectral_flatness_db_mean>=0.271369 then node 3 else 8953
2  class = 8953
3  class = 8934


row =

        8928

Row: 8928, pDepth = 8, loss = 0.099029

Decision tree for classification
1  if silence_rate_30dB_dmean2<0.017544 then node 2 elseif silence_rate_30dB_dmean2>=0.017544 then node 3 else 8904
2  class = 8904
3  class = 8903


row =

        8956

Row: 8956, pDepth = 12, loss = 0.124884

Decision tree for classification
1  if gfcc_median_1<0.520562 then node 2 elseif gfcc_median_1>=0.520562 then node 3 else 8950
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
3  class = 8950
4  class = 8923
5  class = 8950


row =

        8969

Row: 8969, pDepth = 18, loss = 0.185809

Decision tree for classification
 1  if spectral_energy_var<0.0002945 then node 2 elseif spectral_energy_var>=0.0002945 then node 3 else 8960
 2  if second_peak_bpm_min<0.593496 then node 4 elseif second_peak_bpm_min>=0.593496 then node 5 else 8949
 3  if second_peak_bpm_min<0.310976 then node 6 elseif second_peak_bpm_min>=0.310976 then node 7 else 8960
 4  class = 8949
 5  if spectral_energy_var<1.5e-06 then node 8 elseif spectral_energy_var>=1.5e-06 then node 9 else 8949
 6  class = 8949
 7  class = 8960
 8  class = 8949
 9  if spectral_decrease_mean<0.900607 then node 10 elseif spectral_decrease_mean>=0.900607 then node 11 else 8960
10  if strongdecay<0.0611825 then node 12 elseif strongdecay>=0.0611825 then node 13 else 8960
11  class = 8949
12  class = 8960
13  if spectral_decrease_var<3.5e-05 then node 14 elseif spectral_decrease_var>=3.5e-05 then node 15 else 8949
14  if spectral_decrease_mean<0.900432 then node 16 elseif spectral_decrease_mean>=0.900432 then node 17 else 8949
15  class = 8960
16  class = 8949
17  class = 8960


row =

        8971

Row: 8971, pDepth = 23, loss = 0.178851

Decision tree for classification
1  if first_peak_weight_max<0.894445 then node 2 elseif first_peak_weight_max>=0.894445 then node 3 else 8965
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
3  class = 8965
4  class = 8962
5  class = 8965


row =

        8967

Row: 8967, pDepth = 4, loss = 0.037081

Decision tree for classification
1  if spectral_spread_mean<0.015563 then node 2 elseif spectral_spread_mean>=0.015563 then node 3 else 8961
2  class = 8964
3  class = 8961


row =

        8970

Row: 8970, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_spread_mean<0.015563 then node 2 elseif spectral_spread_mean>=0.015563 then node 3 else 8961
2  class = 8964
3  class = 8961


row =

        8863

Row: 8863, pDepth = 3, loss = 0.087912

Decision tree for classification
1  if mfcc_dmean_5<0.243998 then node 2 elseif mfcc_dmean_5>=0.243998 then node 3 else 8745
2  class = 8745
3  class = 8722


row =

        8879

Row: 8879, pDepth = 1, loss = 0.089109

Decision tree for classification
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
2  class = 8693
3  class = 8841


row =

        8934

Row: 8934, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8693
3  class = 8841


row =

        8953

Row: 8953, pDepth = 12, loss = 0.169591

Decision tree for classification
1  if pitch_mean<0.103532 then node 2 elseif pitch_mean>=0.103532 then node 3 else 8939
2  class = 8939
3  class = 8943


row =

        8903

Row: 8903, pDepth = 2, loss = 0.065089

Decision tree for classification
1  if scvalleys_mean_0<0.660545 then node 2 elseif scvalleys_mean_0>=0.660545 then node 3 else 8812
2  class = 8735
3  class = 8812


row =

        8904

Row: 8904, pDepth = 5, loss = 0.127168

Decision tree for classification
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
2  class = 8815
3  class = 8854


row =

        8923

Row: 8923, pDepth = 7, loss = 0.188679

Decision tree for classification
1  if gfcc_dmean_1<0.15129 then node 2 elseif gfcc_dmean_1>=0.15129 then node 3 else 8898
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
3  class = 8898
4  class = 8901
5  class = 8898


row =

        8950

Row: 8950, pDepth = 6, loss = 0.056291

Decision tree for classification
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
2  class = 8922
3  class = 8909


row =

        8949

Row: 8949, pDepth = 11, loss = 0.166455

Decision tree for classification
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
2  class = 8948
3  class = 8926


row =

        8960

Row: 8960, pDepth = 17, loss = 0.197952

Decision tree for classification
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
2  class = 8944
3  class = 8947


row =

        8962

Row: 8962, pDepth = 11, loss = 0.144491

Decision tree for classification
1  if spectral_skewness_median<0.0668925 then node 2 elseif spectral_skewness_median>=0.0668925 then node 3 else 8958
2  class = 8958
3  class = 8931


row =

        8965

Row: 8965, pDepth = 18, loss = 0.195359

Decision tree for classification
1  if scvalleys_mean_0<0.684385 then node 2 elseif scvalleys_mean_0>=0.684385 then node 3 else 8957
2  if scvalleys_min_2<0.395652 then node 4 elseif scvalleys_min_2>=0.395652 then node 5 else 8937
3  class = 8957
4  class = 8937
5  class = 8957


row =

        8961

Row: 8961, pDepth = 3, loss = 0.022222

Decision tree for classification
1  if strongdecay<0.077154 then node 2 elseif strongdecay>=0.077154 then node 3 else 8952
2  class = 8885
3  class = 8952


row =

        8964

Row: 8964, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if strongdecay<0.077154 then node 2 elseif strongdecay>=0.077154 then node 3 else 8952
2  class = 8885
3  class = 8952


row =

        8942

Row: 8942, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if strongdecay<0.077154 then node 2 elseif strongdecay>=0.077154 then node 3 else 8952
2  class = 8885
3  class = 8952


row =

        8968

Row: 8968, pDepth = 14, loss = 0.165846

Decision tree for classification
1  if gfcc_min_2<0.387343 then node 2 elseif gfcc_min_2>=0.387343 then node 3 else 8954
2  class = 8951
3  class = 8954


row =

        8722

Row: 8722, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_min_2<0.387343 then node 2 elseif gfcc_min_2>=0.387343 then node 3 else 8954
2  class = 8951
3  class = 8954


row =

        8745

Row: 8745, pDepth = 1, loss = 0.057971

Decision tree for classification
1  if spectral_energyband_high_max<0.193083 then node 2 elseif spectral_energyband_high_max>=0.193083 then node 3 else 8670
2  class = 8670
3  class = 8018


row =

        8693

Row: 8693, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_energyband_high_max<0.193083 then node 2 elseif spectral_energyband_high_max>=0.193083 then node 3 else 8670
2  class = 8670
3  class = 8018


row =

        8841

Row: 8841, pDepth = 2, loss = 0.181818

Decision tree for classification
1  if zerocrossingrate_dmean2<0.091876 then node 2 elseif zerocrossingrate_dmean2>=0.091876 then node 3 else 8771
2  class = 8771
3  if erb_bands_max_4<0.009823 then node 4 elseif erb_bands_max_4>=0.009823 then node 5 else 8771
4  class = 8794
5  class = 8771


row =

        7760

Row: 7760, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if zerocrossingrate_dmean2<0.091876 then node 2 elseif zerocrossingrate_dmean2>=0.091876 then node 3 else 8771
2  class = 8771
3  if erb_bands_max_4<0.009823 then node 4 elseif erb_bands_max_4>=0.009823 then node 5 else 8771
4  class = 8794
5  class = 8771


row =

        8913

Row: 8913, pDepth = 3, loss = 0.068966

Decision tree for classification
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
2  class = 8875
3  class = 8774


row =

        8939

Row: 8939, pDepth = 5, loss = 0.118834

Decision tree for classification
1  if scvalleys_min_4<0.0737265 then node 2 elseif scvalleys_min_4>=0.0737265 then node 3 else 8925
2  class = 8804
3  class = 8925


row =

        8943

Row: 8943, pDepth = 3, loss = 0.105042

Decision tree for classification
1  if spectral_contrast_dvar_5<0.231631 then node 2 elseif spectral_contrast_dvar_5>=0.231631 then node 3 else 8906
2  class = 8906
3  class = 8911


row =

        8735

Row: 8735, pDepth = 1, loss = 0.051282

Decision tree for classification
1  if inharmonicity_mean<0.0605425 then node 2 elseif inharmonicity_mean>=0.0605425 then node 3 else 8380
2  class = 8171
3  class = 8380


row =

        8812

Row: 8812, pDepth = 2, loss = 0.092308

Decision tree for classification
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
2  class = 8494
3  class = 8759


row =

        8815

Row: 8815, pDepth = 3, loss = 0.137255

Decision tree for classification
1  if scvalleys_mean_2<0.715351 then node 2 elseif scvalleys_mean_2>=0.715351 then node 3 else 8697
2  class = 8697
3  class = 8302


row =

        8854

Row: 8854, pDepth = 2, loss = 0.186528

Decision tree for classification
 1  if scvalleys_dvar_3<0.0380355 then node 2 elseif scvalleys_dvar_3>=0.0380355 then node 3 else 8765
 2  if pitch_dmean2<0.0069445 then node 4 elseif pitch_dmean2>=0.0069445 then node 5 else 8765
 3  if barkbands_dvar2_16<5e-07 then node 6 elseif barkbands_dvar2_16>=5e-07 then node 7 else 8734
 4  class = 8734
 5  if pitch_dmean2<0.10212 then node 8 elseif pitch_dmean2>=0.10212 then node 9 else 8765
 6  if pitch_dmean2<0.133033 then node 10 elseif pitch_dmean2>=0.133033 then node 11 else 8765
 7  if erb_bands_var_14<5e-07 then node 12 elseif erb_bands_var_14>=5e-07 then node 13 else 8734
 8  class = 8765
 9  if scvalleys_dvar_3<0.021109 then node 14 elseif scvalleys_dvar_3>=0.021109 then node 15 else 8734
10  if pitch_dmean2<0.0082305 then node 16 elseif pitch_dmean2>=0.0082305 then node 17 else 8765
11  class = 8734
12  class = 8734
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
14  class = 8734
15  class = 8765
16  class = 8734
17  class = 8765
18  if scvalleys_dvar_3<0.0476555 then node 20 elseif scvalleys_dvar_3>=0.0476555 then node 21 else 8765
19  class = 8734
20  class = 8765
21  class = 8734


row =

        8898

Row: 8898, pDepth = 4, loss = 0.075099

Decision tree for classification
1  if barkbands_dmean_18<0.0001645 then node 2 elseif barkbands_dmean_18>=0.0001645 then node 3 else 8886
2  if barkbands_dmean_15<1.15e-05 then node 4 elseif barkbands_dmean_15>=1.15e-05 then node 5 else 8362
3  class = 8886
4  class = 8886
5  class = 8362


row =

        8901

Row: 8901, pDepth = 4, loss = 0.058036

Decision tree for classification
1  if tristimulus_median_0<0.0025435 then node 2 elseif tristimulus_median_0>=0.0025435 then node 3 else 8860
2  class = 8831
3  class = 8860


row =

        8909

Row: 8909, pDepth = 5, loss = 0.141509

Decision tree for classification
1  if zerocrossingrate_var<0.0237885 then node 2 elseif zerocrossingrate_var>=0.0237885 then node 3 else 8783
2  class = 8741
3  class = 8783


row =

        8922

Row: 8922, pDepth = 6, loss = 0.145408

Decision tree for classification
1  if silence_rate_30dB_mean<0.974647 then node 2 elseif silence_rate_30dB_mean>=0.974647 then node 3 else 8880
2  class = 8858
3  class = 8880


row =

        8926

Row: 8926, pDepth = 4, loss = 0.105263

Decision tree for classification
1  if spectral_spread_dvar2<0.152478 then node 2 elseif spectral_spread_dvar2>=0.152478 then node 3 else 8897
2  class = 8897
3  class = 8881


row =

        8948

Row: 8948, pDepth = 5, loss = 0.164360

Decision tree for classification
1  if first_peak_spread_max<0.099624 then node 2 elseif first_peak_spread_max>=0.099624 then node 3 else 8933
2  class = 8895
3  class = 8933


row =

        8944

Row: 8944, pDepth = 10, loss = 0.182203

Decision tree for classification
1  if spectral_decrease_min<0.975873 then node 2 elseif spectral_decrease_min>=0.975873 then node 3 else 8905
2  class = 8905
3  class = 8888


row =

        8947

Row: 8947, pDepth = 9, loss = 0.137143

Decision tree for classification
1  if scvalleys_min_5<0.329405 then node 2 elseif scvalleys_min_5>=0.329405 then node 3 else 8940
2  class = 8940
3  class = 8893


row =

        8931

Row: 8931, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_min_5<0.329405 then node 2 elseif scvalleys_min_5>=0.329405 then node 3 else 8940
2  class = 8940
3  class = 8893


row =

        8958

Row: 8958, pDepth = 8, loss = 0.084151

Decision tree for classification
1  if scvalleys_max_1<0.574793 then node 2 elseif scvalleys_max_1>=0.574793 then node 3 else 8955
2  class = 8935
3  class = 8955


row =

        8937

Row: 8937, pDepth = 7, loss = 0.156306

Decision tree for classification
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
2  if pitch_salience_mean<0.568618 then node 4 elseif pitch_salience_mean>=0.568618 then node 5 else 8915
3  class = 8920
4  class = 8920
5  class = 8915


row =

        8957

Row: 8957, pDepth = 9, loss = 0.097025

Decision tree for classification
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
2  class = 8936
3  class = 8919


row =

        8885

Row: 8885, pDepth = 1, loss = 0.039370

Decision tree for classification
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
2  class = 8707
3  class = 8760


row =

        8952

Row: 8952, pDepth = 10, loss = 0.158516

Decision tree for classification
1  if first_peak_spread_min<0.010566 then node 2 elseif first_peak_spread_min>=0.010566 then node 3 else 8918
2  class = 8932
3  if scvalleys_min_3<0.337108 then node 4 elseif scvalleys_min_3>=0.337108 then node 5 else 8918
4  class = 8918
5  if second_peak_spread_max<0.459388 then node 6 elseif second_peak_spread_max>=0.459388 then node 7 else 8918
6  class = 8932
7  class = 8918


row =

        8865

Row: 8865, pDepth = 2, loss = 0.060241

Decision tree for classification
1  if spectral_entropy_mean<0.228026 then node 2 elseif spectral_entropy_mean>=0.228026 then node 3 else 8677
2  class = 8540
3  class = 8677


row =

        8945

Row: 8945, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_entropy_mean<0.228026 then node 2 elseif spectral_entropy_mean>=0.228026 then node 3 else 8677
2  class = 8540
3  class = 8677


row =

        8657

Row: 8657, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_entropy_mean<0.228026 then node 2 elseif spectral_entropy_mean>=0.228026 then node 3 else 8677
2  class = 8540
3  class = 8677


row =

        8723

Row: 8723, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_entropy_mean<0.228026 then node 2 elseif spectral_entropy_mean>=0.228026 then node 3 else 8677
2  class = 8540
3  class = 8677


row =

        8951

Row: 8951, pDepth = 5, loss = 0.193900

Decision tree for classification
1  if first_peak_weight_max<0.775 then node 2 elseif first_peak_weight_max>=0.775 then node 3 else 8941
2  class = 8941
3  class = 8938


row =

        8954

Row: 8954, pDepth = 12, loss = 0.196311

Decision tree for classification
1  if zerocrossingrate_mean<0.10335 then node 2 elseif zerocrossingrate_mean>=0.10335 then node 3 else 8929
2  class = 8924
3  class = 8929


row =

        8579

Row: 8579, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if zerocrossingrate_mean<0.10335 then node 2 elseif zerocrossingrate_mean>=0.10335 then node 3 else 8929
2  class = 8924
3  class = 8929


row =

        8628

Row: 8628, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if zerocrossingrate_mean<0.10335 then node 2 elseif zerocrossingrate_mean>=0.10335 then node 3 else 8929
2  class = 8924
3  class = 8929


row =

        8018

Row: 8018, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if zerocrossingrate_mean<0.10335 then node 2 elseif zerocrossingrate_mean>=0.10335 then node 3 else 8929
2  class = 8924
3  class = 8929


row =

        8670

Row: 8670, pDepth = 1, loss = 0.118644

Decision tree for classification
1  if gfcc_median_2<0.442238 then node 2 elseif gfcc_median_2>=0.442238 then node 3 else 8606
2  class = 8606
3  class = 8650


row =

        8184

Row: 8184, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_median_2<0.442238 then node 2 elseif gfcc_median_2>=0.442238 then node 3 else 8606
2  class = 8606
3  class = 8650


row =

        8637

Row: 8637, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_median_2<0.442238 then node 2 elseif gfcc_median_2>=0.442238 then node 3 else 8606
2  class = 8606
3  class = 8650


row =

        8771

Row: 8771, pDepth = 1, loss = 0.035714

Decision tree for classification
1  if erb_bands_dmean_3<0.576388 then node 2 elseif erb_bands_dmean_3>=0.576388 then node 3 else 8633
2  class = 8633
3  class = 8430


row =

        8794

Row: 8794, pDepth = 1, loss = 0.095238

Decision tree for classification
1  if barkbands_median_2<1.65e-05 then node 2 elseif barkbands_median_2>=1.65e-05 then node 3 else 8217
2  class = 8534
3  class = 8217


row =

        5827

Row: 5827, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if barkbands_median_2<1.65e-05 then node 2 elseif barkbands_median_2>=1.65e-05 then node 3 else 8217
2  class = 8534
3  class = 8217


row =

        7156

Row: 7156, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if barkbands_median_2<1.65e-05 then node 2 elseif barkbands_median_2>=1.65e-05 then node 3 else 8217
2  class = 8534
3  class = 8217


row =

        8774

Row: 8774, pDepth = 2, loss = 0.061224

Decision tree for classification
1  if barkbands_mean_17<8.1e-05 then node 2 elseif barkbands_mean_17>=8.1e-05 then node 3 else 8621
2  class = 8621
3  class = 8312


row =

        8875

Row: 8875, pDepth = 3, loss = 0.104000

Decision tree for classification
1  if spectral_flux_max<0.200327 then node 2 elseif spectral_flux_max>=0.200327 then node 3 else 8870
2  class = 8870
3  if pitch_instantaneous_confidence_var<0.108638 then node 4 elseif pitch_instantaneous_confidence_var>=0.108638 then node 5 else 8799
4  class = 8870
5  class = 8799


row =

        8804

Row: 8804, pDepth = 2, loss = 0.138614

Decision tree for classification
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
2  class = 8743
3  class = 8726


row =

        8925

Row: 8925, pDepth = 4, loss = 0.098551

Decision tree for classification
1  if silence_rate_30dB_mean<0.990566 then node 2 elseif silence_rate_30dB_mean>=0.990566 then node 3 else 8900
2  class = 8900
3  class = 8884


row =

        8906

Row: 8906, pDepth = 6, loss = 0.194595

Decision tree for classification
1  if pitch_mean<0.118812 then node 2 elseif pitch_mean>=0.118812 then node 3 else 8856
2  class = 8828
3  class = 8856


row =

        8911

Row: 8911, pDepth = 2, loss = 0.075472

Decision tree for classification
1  if mfcc_dvar_5<0.228067 then node 2 elseif mfcc_dvar_5>=0.228067 then node 3 else 8829
2  class = 8829
3  class = 8821


row =

        8171

Row: 8171, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if mfcc_dvar_5<0.228067 then node 2 elseif mfcc_dvar_5>=0.228067 then node 3 else 8829
2  class = 8829
3  class = 8821


row =

        8380

Row: 8380, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if mfcc_dvar_5<0.228067 then node 2 elseif mfcc_dvar_5>=0.228067 then node 3 else 8829
2  class = 8829
3  class = 8821


row =

        8494

Row: 8494, pDepth = 2, loss = 0.157895

Decision tree for classification
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
2  class = 7417
3  class = 7893


row =

        8759

Row: 8759, pDepth = 2, loss = 0.136986

Decision tree for classification
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
2  class = 8537
3  class = 8497


row =

        8302

Row: 8302, pDepth = 2, loss = 0.114286

Decision tree for classification
1  if max_to_total<0.565705 then node 2 elseif max_to_total>=0.565705 then node 3 else 8014
2  class = 8014
3  class = 7541


row =

        8697

Row: 8697, pDepth = 1, loss = 0.048193

Decision tree for classification
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
2  class = 7797
3  class = 8583


row =

        8734

Row: 8734, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 7797
3  class = 8583


row =

        8765

Row: 8765, pDepth = 2, loss = 0.171875

Decision tree for classification
1  if hfc_mean<0.000376 then node 2 elseif hfc_mean>=0.000376 then node 3 else 8675
2  class = 8488
3  class = 8675


row =

        8362

Row: 8362, pDepth = 1, loss = 0.064516

Decision tree for classification
1  if mfcc_median_0<0.194248 then node 2 elseif mfcc_median_0>=0.194248 then node 3 else 8114
2  class = 8114
3  class = 7889


row =

        8886

Row: 8886, pDepth = 6, loss = 0.171171

Decision tree for classification
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
2  class = 8744
3  if scvalleys_var_5<0.206259 then node 4 elseif scvalleys_var_5>=0.206259 then node 5 else 8848
4  class = 8744
5  class = 8848


row =

        8831

Row: 8831, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8744
3  if scvalleys_var_5<0.206259 then node 4 elseif scvalleys_var_5>=0.206259 then node 5 else 8848
4  class = 8744
5  class = 8848


row =

        8860

Row: 8860, pDepth = 6, loss = 0.181347

Decision tree for classification
1  if gfcc_max_0<0.790436 then node 2 elseif gfcc_max_0>=0.790436 then node 3 else 8807
2  class = 8807
3  class = 8768


row =

        8741

Row: 8741, pDepth = 2, loss = 0.144444

Decision tree for classification
1  if spectral_flatness_db_dmean2<0.12055 then node 2 elseif spectral_flatness_db_dmean2>=0.12055 then node 3 else 8682
2  class = 8682
3  class = 8460


row =

        8783

Row: 8783, pDepth = 2, loss = 0.057377

Decision tree for classification
1  if spectral_entropy_mean<0.717311 then node 2 elseif spectral_entropy_mean>=0.717311 then node 3 else 8698
2  class = 8075
3  class = 8698


row =

        8858

Row: 8858, pDepth = 3, loss = 0.099379

Decision tree for classification
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
2  class = 8755
3  class = 8711


row =

        8880

Row: 8880, pDepth = 4, loss = 0.116883

Decision tree for classification
1  if spectral_entropy_dmean<0.103471 then node 2 elseif spectral_entropy_dmean>=0.103471 then node 3 else 8833
2  class = 8341
3  class = 8833


row =

        8881

Row: 8881, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_entropy_dmean<0.103471 then node 2 elseif spectral_entropy_dmean>=0.103471 then node 3 else 8833
2  class = 8341
3  class = 8833


row =

        8897

Row: 8897, pDepth = 2, loss = 0.135294

Decision tree for classification
 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
 2  if spectral_energy_var<6.65e-05 then node 4 elseif spectral_energy_var>=6.65e-05 then node 5 else 8846
 3  class = 8784
 4  if frequency_bands_median_16<5e-07 then node 6 elseif frequency_bands_median_16>=5e-07 then node 7 else 8846
 5  if frequency_bands_median_16<5e-07 then node 8 elseif frequency_bands_median_16>=5e-07 then node 9 else 8846
 6  class = 8846
 7  if gfcc_mean_1<0.44926 then node 10 elseif gfcc_mean_1>=0.44926 then node 11 else 8846
 8  if gfcc_mean_1<0.614542 then node 12 elseif gfcc_mean_1>=0.614542 then node 13 else 8846
 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
10  class = 8846
11  class = 8784
12  class = 8846
13  if gfcc_mean_1<0.671312 then node 16 elseif gfcc_mean_1>=0.671312 then node 17 else 8784
14  if gfcc_mean_1<0.576564 then node 18 elseif gfcc_mean_1>=0.576564 then node 19 else 8784
15  class = 8846
16  class = 8784
17  class = 8846
18  if gfcc_mean_1<0.361218 then node 20 elseif gfcc_mean_1>=0.361218 then node 21 else 8784
19  class = 8784
20  class = 8784
21  if spectral_energy_var<0.000243 then node 22 elseif spectral_energy_var>=0.000243 then node 23 else 8846
22  class = 8846
23  if spectral_energy_var<0.000607 then node 24 elseif spectral_energy_var>=0.000607 then node 25 else 8784
24  class = 8784
25  class = 8846


row =

        8895

Row: 8895, pDepth = 4, loss = 0.108225

Decision tree for classification
1  if scvalleys_mean_1<0.706145 then node 2 elseif scvalleys_mean_1>=0.706145 then node 3 else 8840
2  class = 8864
3  class = 8840


row =

        8933

Row: 8933, pDepth = 3, loss = 0.051873

Decision tree for classification
1  if silence_rate_60dB_dmean2<0.001268 then node 2 elseif silence_rate_60dB_dmean2>=0.001268 then node 3 else 8872
2  class = 8838
3  class = 8872


row =

        8888

Row: 8888, pDepth = 4, loss = 0.144860

Decision tree for classification
1  if spectral_centroid_median<0.248646 then node 2 elseif spectral_centroid_median>=0.248646 then node 3 else 8816
2  class = 8816
3  class = 8764


row =

        8905

Row: 8905, pDepth = 5, loss = 0.124031

Decision tree for classification
1  if spectral_flatness_db_mean<0.320336 then node 2 elseif spectral_flatness_db_mean>=0.320336 then node 3 else 8868
2  class = 8868
3  class = 8738


row =

        8893

Row: 8893, pDepth = 3, loss = 0.096939

Decision tree for classification
1  if frequency_bands_median_21<0.0001485 then node 2 elseif frequency_bands_median_21>=0.0001485 then node 3 else 8785
2  class = 8785
3  class = 8678


row =

        8940

Row: 8940, pDepth = 9, loss = 0.172619

Decision tree for classification
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
2  class = 8912
3  class = 8876


row =

        8750

Row: 8750, pDepth = 3, loss = 0.172727

Decision tree for classification
1  if first_peak_weight_mean<0.763889 then node 2 elseif first_peak_weight_mean>=0.763889 then node 3 else 8464
2  class = 8720
3  class = 8464


row =

        8902

Row: 8902, pDepth = 2, loss = 0.107914

Decision tree for classification
1  if scvalleys_max_2<0.700581 then node 2 elseif scvalleys_max_2>=0.700581 then node 3 else 8824
2  class = 8790
3  class = 8824


row =

        8935

Row: 8935, pDepth = 1, loss = 0.042781

Decision tree for classification
1  if spectral_entropy_max<0.91212 then node 2 elseif spectral_entropy_max>=0.91212 then node 3 else 8871
2  class = 8835
3  class = 8871


row =

        8955

Row: 8955, pDepth = 7, loss = 0.140684

Decision tree for classification
1  if second_peak_weight_median<0.17782 then node 2 elseif second_peak_weight_median>=0.17782 then node 3 else 8910
2  class = 8910
3  class = 8946


row =

        8915

Row: 8915, pDepth = 6, loss = 0.083056

Decision tree for classification
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
2  class = 8819
3  class = 8837


row =

        8920

Row: 8920, pDepth = 4, loss = 0.133588

Decision tree for classification
1  if barkbands_max_18<0.0004445 then node 2 elseif barkbands_max_18>=0.0004445 then node 3 else 8891
2  class = 8843
3  class = 8891


row =

        8919

Row: 8919, pDepth = 1, loss = 0.072131

Decision tree for classification
1  if first_peak_weight_mean<0.763889 then node 2 elseif first_peak_weight_mean>=0.763889 then node 3 else 8873
2  class = 8852
3  class = 8873


row =

        8936

Row: 8936, pDepth = 6, loss = 0.074786

Decision tree for classification
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
2  class = 8921
3  class = 8857


row =

        8707

Row: 8707, pDepth = 1, loss = 0.022222

Decision tree for classification
1  if second_peak_spread_max<0.050472 then node 2 elseif second_peak_spread_max>=0.050472 then node 3 else 8301
2  class = 8156
3  class = 8301


row =

        8760

Row: 8760, pDepth = 2, loss = 0.109756

Decision tree for classification
1  if barkbands_dmean2_11<7.5e-06 then node 2 elseif barkbands_dmean2_11>=7.5e-06 then node 3 else 8063
2  class = 8327
3  class = 8063


row =

        8918

Row: 8918, pDepth = 7, loss = 0.181818

Decision tree for classification
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
2  class = 8883
3  class = 8869


row =

        8932

Row: 8932, pDepth = 3, loss = 0.052885

Decision tree for classification
1  if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
2  class = 8894
3  class = 8908


row =

        8540

Row: 8540, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
2  class = 8894
3  class = 8908


row =

        8677

Row: 8677, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
2  class = 8894
3  class = 8908


row =

        8474

Row: 8474, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
2  class = 8894
3  class = 8908


row =

        8890

Row: 8890, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
2  class = 8894
3  class = 8908


row =

        8424

Row: 8424, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
2  class = 8894
3  class = 8908


row =

        8127

Row: 8127, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_mean_0<0.837236 then node 2 elseif gfcc_mean_0>=0.837236 then node 3 else 8894
2  class = 8894
3  class = 8908


row =

        8938

Row: 8938, pDepth = 3, loss = 0.105000

Decision tree for classification
1  if inharmonicity_median<0.00494 then node 2 elseif inharmonicity_median>=0.00494 then node 3 else 8896
2  class = 8927
3  class = 8896


row =

        8941

Row: 8941, pDepth = 1, loss = 0.038610

Decision tree for classification
1  if gfcc_mean_1<0.300223 then node 2 elseif gfcc_mean_1>=0.300223 then node 3 else 8882
2  class = 8767
3  class = 8882


row =

        8924

Row: 8924, pDepth = 1, loss = 0.066845

Decision tree for classification
1  if first_peak_spread_median<0.215852 then node 2 elseif first_peak_spread_median>=0.215852 then node 3 else 8914
2  class = 8914
3  class = 8781


row =

        8929

Row: 8929, pDepth = 7, loss = 0.187013

Decision tree for classification
1  if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
2  class = 8916
3  class = 8823


row =

        7850

Row: 7850, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
2  class = 8916
3  class = 8823


row =

        7940

Row: 7940, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
2  class = 8916
3  class = 8823


row =

        8371

Row: 8371, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
2  class = 8916
3  class = 8823


row =

        8530

Row: 8530, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
2  class = 8916
3  class = 8823


row =

        6907

Row: 6907, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
2  class = 8916
3  class = 8823


row =

        7646

Row: 7646, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_var_1<0.081249 then node 2 elseif scvalleys_var_1>=0.081249 then node 3 else 8916
2  class = 8916
3  class = 8823


row =

        8606

Row: 8606, pDepth = 1, loss = 0.066667

Decision tree for classification
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
2  class = 8492
3  class = 8276


row =

        8650

Row: 8650, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8492
3  class = 8276


row =

        7144

Row: 7144, pDepth = 0, loss = 1.000000

Decision tree for classification
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
2  class = 8492
3  class = 8276


row =

        8230

Row: 8230, pDepth = 0, loss = 1.000000

Decision tree for classification
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
2  class = 8492
3  class = 8276


row =

        8471

Row: 8471, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8492
3  class = 8276


row =

        8430

Row: 8430, pDepth = 0, loss = 1.000000

Decision tree for classification
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
2  class = 8492
3  class = 8276


row =

        8633

Row: 8633, pDepth = 1, loss = 0.127660

Decision tree for classification
1  if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
2  class = 8387
3  class = 8563


row =

        8217

Row: 8217, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
2  class = 8387
3  class = 8563


row =

        8534

Row: 8534, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
2  class = 8387
3  class = 8563


row =

   712

Row: 712, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
2  class = 8387
3  class = 8563


row =

        2269

Row: 2269, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
2  class = 8387
3  class = 8563


row =

        4426

Row: 4426, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
2  class = 8387
3  class = 8563


row =

        5110

Row: 5110, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
2  class = 8387
3  class = 8563


row =

        8312

Row: 8312, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
2  class = 8387
3  class = 8563


row =

        8621

Row: 8621, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_flatness_db_max<0.151868 then node 2 elseif spectral_flatness_db_max>=0.151868 then node 3 else 8387
2  class = 8387
3  class = 8563


row =

        8799

Row: 8799, pDepth = 1, loss = 0.145455

Decision tree for classification
1  if mfcc_var_0<0.25508 then node 2 elseif mfcc_var_0>=0.25508 then node 3 else 8719
2  class = 8719
3  class = 8645


row =

        8870

Row: 8870, pDepth = 2, loss = 0.128571

Decision tree for classification
1  if spectral_flatness_db_dmean2<0.165319 then node 2 elseif spectral_flatness_db_dmean2>=0.165319 then node 3 else 8811
2  class = 8811
3  class = 8791


row =

        8726

Row: 8726, pDepth = 1, loss = 0.132075

Decision tree for classification
1  if spectral_spread_var<0.107504 then node 2 elseif spectral_spread_var>=0.107504 then node 3 else 8431
2  class = 8515
3  class = 8431


row =

        8743

Row: 8743, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_spread_var<0.107504 then node 2 elseif spectral_spread_var>=0.107504 then node 3 else 8431
2  class = 8515
3  class = 8431


row =

        8884

Row: 8884, pDepth = 5, loss = 0.149351

Decision tree for classification
1  if gfcc_mean_0<0.755376 then node 2 elseif gfcc_mean_0>=0.755376 then node 3 else 8788
2  class = 8788
3  class = 8826


row =

        8900

Row: 8900, pDepth = 6, loss = 0.125654

Decision tree for classification
1  if barkbands_median_20<0.0007535 then node 2 elseif barkbands_median_20>=0.0007535 then node 3 else 8861
2  class = 8861
3  class = 8721


row =

        8828

Row: 8828, pDepth = 1, loss = 0.065934

Decision tree for classification
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
2  class = 8786
3  class = 8761


row =

        8856

Row: 8856, pDepth = 3, loss = 0.095745

Decision tree for classification
1  if inharmonicity_mean<0.157454 then node 2 elseif inharmonicity_mean>=0.157454 then node 3 else 8731
2  class = 8690
3  class = 8731


row =

        8821

Row: 8821, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if inharmonicity_mean<0.157454 then node 2 elseif inharmonicity_mean>=0.157454 then node 3 else 8731
2  class = 8690
3  class = 8731


row =

        8829

Row: 8829, pDepth = 1, loss = 0.031250

Decision tree for classification
1  if tristimulus_mean_1<0.160731 then node 2 elseif tristimulus_mean_1>=0.160731 then node 3 else 8725
2  class = 8725
3  class = 8619


row =

        6751

Row: 6751, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if tristimulus_mean_1<0.160731 then node 2 elseif tristimulus_mean_1>=0.160731 then node 3 else 8725
2  class = 8725
3  class = 8619


row =

        8021

Row: 8021, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if tristimulus_mean_1<0.160731 then node 2 elseif tristimulus_mean_1>=0.160731 then node 3 else 8725
2  class = 8725
3  class = 8619


row =

        7998

Row: 7998, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if tristimulus_mean_1<0.160731 then node 2 elseif tristimulus_mean_1>=0.160731 then node 3 else 8725
2  class = 8725
3  class = 8619


row =

        8275

Row: 8275, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if tristimulus_mean_1<0.160731 then node 2 elseif tristimulus_mean_1>=0.160731 then node 3 else 8725
2  class = 8725
3  class = 8619


row =

        7417

Row: 7417, pDepth = 1, loss = 0.060606

Decision tree for classification
1  if spectral_entropy_dmean2<0.119373 then node 2 elseif spectral_entropy_dmean2>=0.119373 then node 3 else 6660
2  class = 6662
3  class = 6660


row =

        7893

Row: 7893, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_entropy_dmean2<0.119373 then node 2 elseif spectral_entropy_dmean2>=0.119373 then node 3 else 6660
2  class = 6662
3  class = 6660


row =

        8497

Row: 8497, pDepth = 1, loss = 0.074074

Decision tree for classification
1  if spectral_rms_mean<0.026824 then node 2 elseif spectral_rms_mean>=0.026824 then node 3 else 7955
2  class = 7702
3  class = 7955


row =

        8537

Row: 8537, pDepth = 2, loss = 0.108696

Decision tree for classification
1  if spectral_entropy_min<0.583247 then node 2 elseif spectral_entropy_min>=0.583247 then node 3 else 8179
2  class = 8069
3  class = 8179


row =

        7541

Row: 7541, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_entropy_min<0.583247 then node 2 elseif spectral_entropy_min>=0.583247 then node 3 else 8179
2  class = 8069
3  class = 8179


row =

        8014

Row: 8014, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_entropy_min<0.583247 then node 2 elseif spectral_entropy_min>=0.583247 then node 3 else 8179
2  class = 8069
3  class = 8179


row =

        7797

Row: 7797, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_entropy_min<0.583247 then node 2 elseif spectral_entropy_min>=0.583247 then node 3 else 8179
2  class = 8069
3  class = 8179


row =

        8583

Row: 8583, pDepth = 1, loss = 0.098592

Decision tree for classification
1  if zerocrossingrate_min<0.0510405 then node 2 elseif zerocrossingrate_min>=0.0510405 then node 3 else 8289
2  class = 8245
3  class = 8289


row =

        7922

Row: 7922, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if zerocrossingrate_min<0.0510405 then node 2 elseif zerocrossingrate_min>=0.0510405 then node 3 else 8289
2  class = 8245
3  class = 8289


row =

        8708

Row: 8708, pDepth = 2, loss = 0.053571

Decision tree for classification
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
2  class = 8469
3  class = 8370


row =

        8488

Row: 8488, pDepth = 1, loss = 0.132075

Decision tree for classification
1  if scvalleys_dvar_1<0.0257925 then node 2 elseif scvalleys_dvar_1>=0.0257925 then node 3 else 8201
2  class = 8201
3  class = 8393


row =

        8675

Row: 8675, pDepth = 1, loss = 0.133333

Decision tree for classification
1  if mfcc_dvar_0<0.103958 then node 2 elseif mfcc_dvar_0>=0.103958 then node 3 else 8592
2  class = 7871
3  class = 8592


row =

        7889

Row: 7889, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if mfcc_dvar_0<0.103958 then node 2 elseif mfcc_dvar_0>=0.103958 then node 3 else 8592
2  class = 7871
3  class = 8592


row =

        8114

Row: 8114, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if mfcc_dvar_0<0.103958 then node 2 elseif mfcc_dvar_0>=0.103958 then node 3 else 8592
2  class = 7871
3  class = 8592


row =

        8744

Row: 8744, pDepth = 2, loss = 0.095238

Decision tree for classification
1  if scvalleys_var_2<0.120504 then node 2 elseif scvalleys_var_2>=0.120504 then node 3 else 8508
2  class = 8508
3  class = 8569


row =

        8848

Row: 8848, pDepth = 5, loss = 0.179487

Decision tree for classification
1  if spectral_flatness_db_dmean<0.184358 then node 2 elseif spectral_flatness_db_dmean>=0.184358 then node 3 else 8777
2  class = 8777
3  class = 8801


row =

        7522

Row: 7522, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_flatness_db_dmean<0.184358 then node 2 elseif spectral_flatness_db_dmean>=0.184358 then node 3 else 8777
2  class = 8777
3  class = 8801


row =

        8510

Row: 8510, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_flatness_db_dmean<0.184358 then node 2 elseif spectral_flatness_db_dmean>=0.184358 then node 3 else 8777
2  class = 8777
3  class = 8801


row =

        8768

Row: 8768, pDepth = 2, loss = 0.098901

Decision tree for classification
1  if silence_rate_30dB_dmean2<0.0486295 then node 2 elseif silence_rate_30dB_dmean2>=0.0486295 then node 3 else 8425
2  class = 8425
3  class = 8612


row =

        8807

Row: 8807, pDepth = 2, loss = 0.078431

Decision tree for classification
1  if inharmonicity_mean<0.046759 then node 2 elseif inharmonicity_mean>=0.046759 then node 3 else 8724
2  class = 8299
3  class = 8724


row =

        8460

Row: 8460, pDepth = 1, loss = 0.085714

Decision tree for classification
1  if spectral_contrast_mean_5<0.195317 then node 2 elseif spectral_contrast_mean_5>=0.195317 then node 3 else 8377
2  class = 8205
3  class = 8377


row =

        8682

Row: 8682, pDepth = 1, loss = 0.018182

Decision tree for classification
1  if barkbands_spread_dvar<0.0142395 then node 2 elseif barkbands_spread_dvar>=0.0142395 then node 3 else 8588
2  class = 6448
3  class = 8588


row =

        8075

Row: 8075, pDepth = 1, loss = 0.033333

Decision tree for classification
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
2  class = 7659
3  class = 7015


row =

        8698

Row: 8698, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 7659
3  class = 7015


row =

        8711

Row: 8711, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 7659
3  class = 7015


row =

        8755

Row: 8755, pDepth = 2, loss = 0.197531

Decision tree for classification
1  if tristimulus_max_2<0.752637 then node 2 elseif tristimulus_max_2>=0.752637 then node 3 else 8658
2  class = 8658
3  if tristimulus_max_2<0.80408 then node 4 elseif tristimulus_max_2>=0.80408 then node 5 else 8658
4  if barkbands_dmean2_22<0.0004235 then node 6 elseif barkbands_dmean2_22>=0.0004235 then node 7 else 8382
5  if erb_bands_dvar2_8<0.0050135 then node 8 elseif erb_bands_dvar2_8>=0.0050135 then node 9 else 8658
6  class = 8382
7  class = 8658
8  class = 8658
9  class = 8382


row =

        8341

Row: 8341, pDepth = 1, loss = 0.079365

Decision tree for classification
1  if scvalleys_var_1<0.38783 then node 2 elseif scvalleys_var_1>=0.38783 then node 3 else 8038
2  class = 5730
3  class = 8038


row =

        8833

Row: 8833, pDepth = 5, loss = 0.142857

Decision tree for classification
1  if scvalleys_dmean_1<0.446352 then node 2 elseif scvalleys_dmean_1>=0.446352 then node 3 else 8758
2  class = 8758
3  class = 8740


row =

        8632

Row: 8632, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_dmean_1<0.446352 then node 2 elseif scvalleys_dmean_1>=0.446352 then node 3 else 8758
2  class = 8758
3  class = 8740


row =

        8769

Row: 8769, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_dmean_1<0.446352 then node 2 elseif scvalleys_dmean_1>=0.446352 then node 3 else 8758
2  class = 8758
3  class = 8740


row =

        8784

Row: 8784, pDepth = 2, loss = 0.166667

Decision tree for classification
1  if pitch_instantaneous_confidence_median<0.454039 then node 2 elseif pitch_instantaneous_confidence_median>=0.454039 then node 3 else 8396
2  class = 8396
3  class = 8689


row =

        8846

Row: 8846, pDepth = 2, loss = 0.048077

Decision tree for classification
1  if mfcc_dvar_7<0.041849 then node 2 elseif mfcc_dvar_7>=0.041849 then node 3 else 8694
2  class = 8694
3  class = 8671


row =

        8840

Row: 8840, pDepth = 4, loss = 0.160000

Decision tree for classification
1  if scvalleys_min_5<0.30358 then node 2 elseif scvalleys_min_5>=0.30358 then node 3 else 8557
2  class = 8557
3  class = 8626


row =

        8864

Row: 8864, pDepth = 2, loss = 0.132075

Decision tree for classification
1  if barkbands_spread_dmean<0.233654 then node 2 elseif barkbands_spread_dmean>=0.233654 then node 3 else 8813
2  class = 8813
3  class = 8566


row =

        8838

Row: 8838, pDepth = 4, loss = 0.113636

Decision tree for classification
1  if second_peak_weight_min<0.310345 then node 2 elseif second_peak_weight_min>=0.310345 then node 3 else 8780
2  class = 8780
3  class = 8603


row =

        8872

Row: 8872, pDepth = 6, loss = 0.146718

Decision tree for classification
1  if second_peak_spread_median<0.188947 then node 2 elseif second_peak_spread_median>=0.188947 then node 3 else 8842
2  if scvalleys_min_4<0.380769 then node 4 elseif scvalleys_min_4>=0.380769 then node 5 else 8789
3  class = 8842
4  class = 8789
5  class = 8842


row =

        8764

Row: 8764, pDepth = 1, loss = 0.162162

Decision tree for classification
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
2  class = 8656
3  class = 8667


row =

        8816

Row: 8816, pDepth = 2, loss = 0.092857

Decision tree for classification
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
2  class = 8746
3  class = 8699


row =

        8738

Row: 8738, pDepth = 1, loss = 0.156863

Decision tree for classification
1  if mfcc_var_10<0.087524 then node 2 elseif mfcc_var_10>=0.087524 then node 3 else 8640
2  class = 8692
3  class = 8640


row =

        8868

Row: 8868, pDepth = 4, loss = 0.130435

Decision tree for classification
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
2  class = 8792
3  class = 8714


row =

        8678

Row: 8678, pDepth = 2, loss = 0.162500

Decision tree for classification
1  if spectral_entropy_max<0.930671 then node 2 elseif spectral_entropy_max>=0.930671 then node 3 else 8531
2  class = 8378
3  class = 8531


row =

        8785

Row: 8785, pDepth = 2, loss = 0.077586

Decision tree for classification
1  if silence_rate_30dB_mean<0.974679 then node 2 elseif silence_rate_30dB_mean>=0.974679 then node 3 else 8702
2  class = 8604
3  class = 8702


row =

        8876

Row: 8876, pDepth = 3, loss = 0.094527

Decision tree for classification
1  if spectral_centroid_mean<0.224613 then node 2 elseif spectral_centroid_mean>=0.224613 then node 3 else 8862
2  class = 8862
3  class = 8386


row =

        8912

Row: 8912, pDepth = 5, loss = 0.184818

Decision tree for classification
1  if stopFrame<0.011142 then node 2 elseif stopFrame>=0.011142 then node 3 else 8899
2  class = 8737
3  class = 8899


row =

        8464

Row: 8464, pDepth = 1, loss = 0.081967

Decision tree for classification
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
2  class = 8361
3  class = 7673


row =

        8720

Row: 8720, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8361
3  class = 7673


row =

        8790

Row: 8790, pDepth = 1, loss = 0.196970

Decision tree for classification
1  if barkbands_median_16<5.5e-06 then node 2 elseif barkbands_median_16>=5.5e-06 then node 3 else 8700
2  class = 8700
3  class = 8433


row =

        8824

Row: 8824, pDepth = 1, loss = 0.054795

Decision tree for classification
1  if mfcc_median_9<0.605751 then node 2 elseif mfcc_median_9>=0.605751 then node 3 else 8666
2  class = 8666
3  class = 8610


row =

        8835

Row: 8835, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if mfcc_median_9<0.605751 then node 2 elseif mfcc_median_9>=0.605751 then node 3 else 8666
2  class = 8666
3  class = 8610


row =

        8871

Row: 8871, pDepth = 2, loss = 0.065789

Decision tree for classification
1  if inharmonicity_mean<0.193569 then node 2 elseif inharmonicity_mean>=0.193569 then node 3 else 8796
2  class = 8796
3  class = 8709


row =

        8910

Row: 8910, pDepth = 3, loss = 0.059896

Decision tree for classification
1  if spectral_decrease_median<0.899888 then node 2 elseif spectral_decrease_median>=0.899888 then node 3 else 8878
2  class = 8810
3  class = 8878


row =

        8946

Row: 8946, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_decrease_median<0.899888 then node 2 elseif spectral_decrease_median>=0.899888 then node 3 else 8878
2  class = 8810
3  class = 8878


row =

        8819

Row: 8819, pDepth = 3, loss = 0.118110

Decision tree for classification
1  if spectral_centroid_median<0.16468 then node 2 elseif spectral_centroid_median>=0.16468 then node 3 else 8648
2  class = 8590
3  class = 8648


row =

        8837

Row: 8837, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_centroid_median<0.16468 then node 2 elseif spectral_centroid_median>=0.16468 then node 3 else 8648
2  class = 8590
3  class = 8648


row =

        8843

Row: 8843, pDepth = 1, loss = 0.065217

Decision tree for classification
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
2  class = 8713
3  class = 8673


row =

        8891

Row: 8891, pDepth = 2, loss = 0.076471

Decision tree for classification
1  if spectral_entropy_mean<0.712979 then node 2 elseif spectral_entropy_mean>=0.712979 then node 3 else 8851
2  class = 8742
3  class = 8851


row =

        8852

Row: 8852, pDepth = 1, loss = 0.053763

Decision tree for classification
1  if scvalleys_mean_2<0.799317 then node 2 elseif scvalleys_mean_2>=0.799317 then node 3 else 8618
2  class = 8458
3  class = 8618


row =

        8873

Row: 8873, pDepth = 5, loss = 0.188679

Decision tree for classification
1  if gfcc_max_0<0.8201 then node 2 elseif gfcc_max_0>=0.8201 then node 3 else 8830
2  class = 8817
3  class = 8830


row =

        8857

Row: 8857, pDepth = 3, loss = 0.078740

Decision tree for classification
1  if silence_rate_30dB_dvar<0.0081965 then node 2 elseif silence_rate_30dB_dvar>=0.0081965 then node 3 else 8800
2  class = 8521
3  class = 8800


row =

        8921

Row: 8921, pDepth = 9, loss = 0.196481

Decision tree for classification
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
2  class = 8889
3  class = 8845


row =

        8156

Row: 8156, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8889
3  class = 8845


row =

        8301

Row: 8301, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8889
3  class = 8845


row =

        8063

Row: 8063, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8889
3  class = 8845


row =

        8327

Row: 8327, pDepth = 1, loss = 0.055556

Decision tree for classification
1  if scvalleys_min_3<0.3427 then node 2 elseif scvalleys_min_3>=0.3427 then node 3 else 7995
2  class = 7995
3  class = 6675


row =

        8869

Row: 8869, pDepth = 4, loss = 0.113990

Decision tree for classification
1  if spectral_entropy_min<0.606841 then node 2 elseif spectral_entropy_min>=0.606841 then node 3 else 8820
2  class = 8820
3  class = 8669


row =

        8883

Row: 8883, pDepth = 5, loss = 0.197917

Decision tree for classification
1  if scvalleys_min_2<0.498872 then node 2 elseif scvalleys_min_2>=0.498872 then node 3 else 8795
2  class = 8795
3  class = 8859


row =

        8894

Row: 8894, pDepth = 2, loss = 0.064286

Decision tree for classification
1  if inharmonicity_var<0.003146 then node 2 elseif inharmonicity_var>=0.003146 then node 3 else 8844
2  class = 8834
3  class = 8844


row =

        8908

Row: 8908, pDepth = 3, loss = 0.161765

Decision tree for classification
1  if gfcc_dmean_0<0.024685 then node 2 elseif gfcc_dmean_0>=0.024685 then node 3 else 8853
2  class = 8849
3  class = 8853


row =

        7240

Row: 7240, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_0<0.024685 then node 2 elseif gfcc_dmean_0>=0.024685 then node 3 else 8853
2  class = 8849
3  class = 8853


row =

        8053

Row: 8053, pDepth = 1, loss = 0.045455

Decision tree for classification
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
2  class = 7824
3  class = 6868


row =

        7891

Row: 7891, pDepth = 0, loss = 1.000000

Decision tree for classification
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
2  class = 7824
3  class = 6868


row =

        8602

Row: 8602, pDepth = 1, loss = 0.081633

Decision tree for classification
1  if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
2  class = 8539
3  class = 8172


row =

        1755

Row: 1755, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
2  class = 8539
3  class = 8172


row =

        7828

Row: 7828, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
2  class = 8539
3  class = 8172


row =

        8688

Row: 8688, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
2  class = 8539
3  class = 8172


row =

        8808

Row: 8808, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
2  class = 8539
3  class = 8172


row =

        6569

Row: 6569, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
2  class = 8539
3  class = 8172


row =

        6932

Row: 6932, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if dissonance_median<0.857549 then node 2 elseif dissonance_median>=0.857549 then node 3 else 8539
2  class = 8539
3  class = 8172


row =

        8896

Row: 8896, pDepth = 1, loss = 0.017544

Decision tree for classification
1  if spectral_rms_mean<0.162661 then node 2 elseif spectral_rms_mean>=0.162661 then node 3 else 8806
2  class = 8806
3  class = 8687


row =

        8927

Row: 8927, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_rms_mean<0.162661 then node 2 elseif spectral_rms_mean>=0.162661 then node 3 else 8806
2  class = 8806
3  class = 8687


row =

        8767

Row: 8767, pDepth = 1, loss = 0.105263

Decision tree for classification
1  if second_peak_bpm_max<0.666667 then node 2 elseif second_peak_bpm_max>=0.666667 then node 3 else 8736
2  class = 8736
3  class = 7749


row =

        8882

Row: 8882, pDepth = 4, loss = 0.120219

Decision tree for classification
1  if scvalleys_min_3<0.370889 then node 2 elseif scvalleys_min_3>=0.370889 then node 3 else 8874
2  class = 8874
3  class = 8805


row =

        8781

Row: 8781, pDepth = 4, loss = 0.179245

Decision tree for classification
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
2  class = 8654
3  class = 8706


row =

        8914

Row: 8914, pDepth = 6, loss = 0.141791

Decision tree for classification
1  if pitch_instantaneous_confidence_mean<0.731062 then node 2 elseif pitch_instantaneous_confidence_mean>=0.731062 then node 3 else 8887
2  class = 8887
3  class = 8822


row =

        8823

Row: 8823, pDepth = 2, loss = 0.147059

Decision tree for classification
1  if barkbands_median_9<2.15e-05 then node 2 elseif barkbands_median_9>=2.15e-05 then node 3 else 8793
2  class = 8593
3  class = 8793


row =

        8916

Row: 8916, pDepth = 3, loss = 0.076305

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        6869

Row: 6869, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        7664

Row: 7664, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        8071

Row: 8071, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        8111

Row: 8111, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        7518

Row: 7518, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        7834

Row: 7834, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        6008

Row: 6008, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        6260

Row: 6260, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        7257

Row: 7257, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        8276

Row: 8276, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        8492

Row: 8492, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        8412

Row: 8412, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        8517

Row: 8517, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        6450

Row: 6450, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        6973

Row: 6973, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        7691

Row: 7691, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        7528

Row: 7528, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        8235

Row: 8235, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        7471

Row: 7471, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        7963

Row: 7963, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        8387

Row: 8387, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_dmean_7<0.189042 then node 2 elseif gfcc_dmean_7>=0.189042 then node 3 else 8907
2  class = 8907
3  if tristimulus_dvar2_2<0.158203 then node 4 elseif tristimulus_dvar2_2>=0.158203 then node 5 else 8867
4  class = 8867
5  class = 8907


row =

        8563

Row: 8563, pDepth = 1, loss = 0.181818

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        7277

Row: 7277, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        7408

Row: 7408, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        8310

Row: 8310, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        8348

Row: 8348, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        1788

Row: 1788, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

   928

Row: 928, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        2329

Row: 2329, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        7614

Row: 7614, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        8046

Row: 8046, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        8491

Row: 8491, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        8614

Row: 8614, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        8645

Row: 8645, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if barkbands_var_19<5e-07 then node 2 elseif barkbands_var_19>=5e-07 then node 3 else 8411
2  class = 7936
3  class = 8411


row =

        8719

Row: 8719, pDepth = 2, loss = 0.178571

Decision tree for classification
1  if spectral_rolloff_median<0.122574 then node 2 elseif spectral_rolloff_median>=0.122574 then node 3 else 8571
2  class = 8571
3  class = 8639


row =

        8791

Row: 8791, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_rolloff_median<0.122574 then node 2 elseif spectral_rolloff_median>=0.122574 then node 3 else 8571
2  class = 8571
3  class = 8639


row =

        8811

Row: 8811, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_rolloff_median<0.122574 then node 2 elseif spectral_rolloff_median>=0.122574 then node 3 else 8571
2  class = 8571
3  class = 8639


row =

        8431

Row: 8431, pDepth = 1, loss = 0.062500

Decision tree for classification
1  if spectral_rms_mean<0.076959 then node 2 elseif spectral_rms_mean>=0.076959 then node 3 else 8228
2  class = 8228
3  class = 7946


row =

        8515

Row: 8515, pDepth = 1, loss = 0.142857

Decision tree for classification
1  if gfcc_mean_3<0.478753 then node 2 elseif gfcc_mean_3>=0.478753 then node 3 else 7558
2  class = 7558
3  if dissonance_min<0.453499 then node 4 elseif dissonance_min>=0.453499 then node 5 else 8186
4  class = 7558
5  class = 8186


row =

        6797

Row: 6797, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_mean_3<0.478753 then node 2 elseif gfcc_mean_3>=0.478753 then node 3 else 7558
2  class = 7558
3  if dissonance_min<0.453499 then node 4 elseif dissonance_min>=0.453499 then node 5 else 8186
4  class = 7558
5  class = 8186


row =

        8500

Row: 8500, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_mean_3<0.478753 then node 2 elseif gfcc_mean_3>=0.478753 then node 3 else 7558
2  class = 7558
3  if dissonance_min<0.453499 then node 4 elseif dissonance_min>=0.453499 then node 5 else 8186
4  class = 7558
5  class = 8186


row =

        8788

Row: 8788, pDepth = 2, loss = 0.181818

Decision tree for classification
1  if spectral_contrast_median_0<0.425454 then node 2 elseif spectral_contrast_median_0>=0.425454 then node 3 else 8643
2  class = 8643
3  if spectral_kurtosis_dmean2<0.0004385 then node 4 elseif spectral_kurtosis_dmean2>=0.0004385 then node 5 else 8303
4  class = 8643
5  class = 8303


row =

        8826

Row: 8826, pDepth = 2, loss = 0.103896

Decision tree for classification
1  if spectral_contrast_median_2<0.304088 then node 2 elseif spectral_contrast_median_2>=0.304088 then node 3 else 8620
2  class = 8620
3  class = 8778


row =

        8721

Row: 8721, pDepth = 2, loss = 0.107143

Decision tree for classification
1  if spectral_contrast_dmean2_4<0.105466 then node 2 elseif spectral_contrast_dmean2_4>=0.105466 then node 3 else 8576
2  class = 8576
3  class = 8591


row =

        8861

Row: 8861, pDepth = 2, loss = 0.111111

Decision tree for classification
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
2  class = 8733
3  class = 8625


row =

        8761

Row: 8761, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8733
3  class = 8625


row =

        8786

Row: 8786, pDepth = 1, loss = 0.053571

Decision tree for classification
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
2  class = 8664
3  class = 8452


row =

        8690

Row: 8690, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8664
3  class = 8452


row =

        8731

Row: 8731, pDepth = 3, loss = 0.078431

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        8608

Row: 8608, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        8772

Row: 8772, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        8619

Row: 8619, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        8725

Row: 8725, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        3025

Row: 3025, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        5585

Row: 5585, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        7034

Row: 7034, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        7190

Row: 7190, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        5191

Row: 5191, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        7093

Row: 7093, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        7882

Row: 7882, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        7908

Row: 7908, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_median_2<0.822059 then node 2 elseif scvalleys_median_2>=0.822059 then node 3 else 8522
2  class = 8522
3  class = 8547


row =

        6660

Row: 6660, pDepth = 1, loss = 0.111111

Decision tree for classification
1  if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
2  class = 4035
3  class = 6376


row =

        6662

Row: 6662, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
2  class = 4035
3  class = 6376


row =

        6659

Row: 6659, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
2  class = 4035
3  class = 6376


row =

        7744

Row: 7744, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
2  class = 4035
3  class = 6376


row =

        7702

Row: 7702, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
2  class = 4035
3  class = 6376


row =

        7955

Row: 7955, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
2  class = 4035
3  class = 6376


row =

        8069

Row: 8069, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
2  class = 4035
3  class = 6376


row =

        8179

Row: 8179, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
2  class = 4035
3  class = 6376


row =

        5426

Row: 5426, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
2  class = 4035
3  class = 6376


row =

        7248

Row: 7248, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
2  class = 4035
3  class = 6376


row =

        3551

Row: 3551, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_mean_1<0.234135 then node 2 elseif spectral_contrast_mean_1>=0.234135 then node 3 else 4035
2  class = 4035
3  class = 6376


row =

        7619

Row: 7619, pDepth = 1, loss = 0.075000

Decision tree for classification
1  if tristimulus_var_0<0.25687 then node 2 elseif tristimulus_var_0>=0.25687 then node 3 else 6997
2  class = 6500
3  class = 6997


row =

        6045

Row: 6045, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if tristimulus_var_0<0.25687 then node 2 elseif tristimulus_var_0>=0.25687 then node 3 else 6997
2  class = 6500
3  class = 6997


row =

        6570

Row: 6570, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if tristimulus_var_0<0.25687 then node 2 elseif tristimulus_var_0>=0.25687 then node 3 else 6997
2  class = 6500
3  class = 6997


row =

        8245

Row: 8245, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if tristimulus_var_0<0.25687 then node 2 elseif tristimulus_var_0>=0.25687 then node 3 else 6997
2  class = 6500
3  class = 6997


row =

        8289

Row: 8289, pDepth = 1, loss = 0.142857

Decision tree for classification
1  if pitch_instantaneous_confidence_mean<0.493124 then node 2 elseif pitch_instantaneous_confidence_mean>=0.493124 then node 3 else 7695
2  class = 7695
3  class = 8058


row =

        7078

Row: 7078, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if pitch_instantaneous_confidence_mean<0.493124 then node 2 elseif pitch_instantaneous_confidence_mean>=0.493124 then node 3 else 7695
2  class = 7695
3  class = 8058


row =

        7253

Row: 7253, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if pitch_instantaneous_confidence_mean<0.493124 then node 2 elseif pitch_instantaneous_confidence_mean>=0.493124 then node 3 else 7695
2  class = 7695
3  class = 8058


row =

        8370

Row: 8370, pDepth = 1, loss = 0.058824

Decision tree for classification
1  if mfcc_max_12<0.311522 then node 2 elseif mfcc_max_12>=0.311522 then node 3 else 8292
2  class = 8292
3  class = 7864


row =

        8469

Row: 8469, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if mfcc_max_12<0.311522 then node 2 elseif mfcc_max_12>=0.311522 then node 3 else 8292
2  class = 8292
3  class = 7864


row =

        8201

Row: 8201, pDepth = 1, loss = 0.125000

Decision tree for classification
1  if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
2  class = 7464
3  class = 7626


row =

        8393

Row: 8393, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
2  class = 7464
3  class = 7626


row =

        7871

Row: 7871, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
2  class = 7464
3  class = 7626


row =

        8592

Row: 8592, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
2  class = 7464
3  class = 7626


row =

        3904

Row: 3904, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
2  class = 7464
3  class = 7626


row =

        6962

Row: 6962, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
2  class = 7464
3  class = 7626


row =

        7624

Row: 7624, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
2  class = 7464
3  class = 7626


row =

        7628

Row: 7628, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_kurtosis_mean<1.25e-05 then node 2 elseif spectral_kurtosis_mean>=1.25e-05 then node 3 else 7626
2  class = 7464
3  class = 7626


row =

        8508

Row: 8508, pDepth = 1, loss = 0.089286

Decision tree for classification
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
2  class = 7671
3  class = 8410


row =

        8569

Row: 8569, pDepth = 2, loss = 0.102041

Decision tree for classification
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
2  class = 7677
3  class = 8268


row =

        8777

Row: 8777, pDepth = 1, loss = 0.058824

Decision tree for classification
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
2  class = 8599
3  class = 8455


row =

        8801

Row: 8801, pDepth = 1, loss = 0.081633

Decision tree for classification
1  if inharmonicity_dvar<0.299663 then node 2 elseif inharmonicity_dvar>=0.299663 then node 3 else 8481
2  class = 8481
3  class = 8444


row =

        6733

Row: 6733, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if inharmonicity_dvar<0.299663 then node 2 elseif inharmonicity_dvar>=0.299663 then node 3 else 8481
2  class = 8481
3  class = 8444


row =

        7333

Row: 7333, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if inharmonicity_dvar<0.299663 then node 2 elseif inharmonicity_dvar>=0.299663 then node 3 else 8481
2  class = 8481
3  class = 8444


row =

        6473

Row: 6473, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if inharmonicity_dvar<0.299663 then node 2 elseif inharmonicity_dvar>=0.299663 then node 3 else 8481
2  class = 8481
3  class = 8444


row =

        8280

Row: 8280, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if inharmonicity_dvar<0.299663 then node 2 elseif inharmonicity_dvar>=0.299663 then node 3 else 8481
2  class = 8481
3  class = 8444


row =

        8425

Row: 8425, pDepth = 1, loss = 0.081633

Decision tree for classification
1  if gfcc_dmean_1<0.107409 then node 2 elseif gfcc_dmean_1>=0.107409 then node 3 else 7683
2  class = 8160
3  class = 7683


row =

        8612

Row: 8612, pDepth = 1, loss = 0.047619

Decision tree for classification
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
2  class = 8269
3  class = 8078


row =

        8299

Row: 8299, pDepth = 1, loss = 0.033333

Decision tree for classification
1  if spectral_contrast_mean_4<0.303843 then node 2 elseif spectral_contrast_mean_4>=0.303843 then node 3 else 7954
2  class = 7954
3  class = 7753


row =

        8724

Row: 8724, pDepth = 2, loss = 0.111111

Decision tree for classification
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
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
3  class = 8509
4  class = 8499
5  class = 8509


row =

        8205

Row: 8205, pDepth = 1, loss = 1.000000

Decision tree for classification
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
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
3  class = 8509
4  class = 8499
5  class = 8509


row =

        8377

Row: 8377, pDepth = 1, loss = 1.000000

Decision tree for classification
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
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
3  class = 8509
4  class = 8499
5  class = 8509


row =

        6448

Row: 6448, pDepth = 1, loss = 1.000000

Decision tree for classification
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
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
3  class = 8509
4  class = 8499
5  class = 8509


row =

        8588

Row: 8588, pDepth = 1, loss = 0.022727

Decision tree for classification
1  if spectral_contrast_max_2<0.244373 then node 2 elseif spectral_contrast_max_2>=0.244373 then node 3 else 7832
2  class = 7942
3  class = 7832


row =

        7015

Row: 7015, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_max_2<0.244373 then node 2 elseif spectral_contrast_max_2>=0.244373 then node 3 else 7832
2  class = 7942
3  class = 7832


row =

        7659

Row: 7659, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_contrast_max_2<0.244373 then node 2 elseif spectral_contrast_max_2>=0.244373 then node 3 else 7832
2  class = 7942
3  class = 7832


row =

        8501

Row: 8501, pDepth = 1, loss = 0.023810

Decision tree for classification
1  if barkbands_mean_26<0.0001275 then node 2 elseif barkbands_mean_26>=0.0001275 then node 3 else 7668
2  class = 7668
3  class = 8122


row =

        8535

Row: 8535, pDepth = 1, loss = 0.080000

Decision tree for classification
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
2  class = 7750
3  class = 8353


row =

        8142

Row: 8142, pDepth = 1, loss = 0.162791

Decision tree for classification
 1  if barkbands_var_17<1.2e-05 then node 2 elseif barkbands_var_17>=1.2e-05 then node 3 else 7798
 2  if frequency_bands_dmean2_12<0.0042235 then node 4 elseif frequency_bands_dmean2_12>=0.0042235 then node 5 else 7798
 3  class = 7917
 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
 5  class = 7917
 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
 7  class = 7798
 8  class = 7798
 9  if frequency_bands_dmean2_12<0.0019425 then node 10 elseif frequency_bands_dmean2_12>=0.0019425 then node 11 else 7917
10  class = 7917
11  class = 7798


row =

        8368

Row: 8368, pDepth = 1, loss = 1.000000

Decision tree for classification
 1  if barkbands_var_17<1.2e-05 then node 2 elseif barkbands_var_17>=1.2e-05 then node 3 else 7798
 2  if frequency_bands_dmean2_12<0.0042235 then node 4 elseif frequency_bands_dmean2_12>=0.0042235 then node 5 else 7798
 3  class = 7917
 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
 5  class = 7917
 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
 7  class = 7798
 8  class = 7798
 9  if frequency_bands_dmean2_12<0.0019425 then node 10 elseif frequency_bands_dmean2_12>=0.0019425 then node 11 else 7917
10  class = 7917
11  class = 7798


row =

        8382

Row: 8382, pDepth = 1, loss = 0.115385

Decision tree for classification
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
2  if barkbands_var_4<0.0031125 then node 4 elseif barkbands_var_4>=0.0031125 then node 5 else 6636
3  if barkbands_var_4<1.2e-05 then node 6 elseif barkbands_var_4>=1.2e-05 then node 7 else 7101
4  class = 6636
5  class = 7101
6  class = 6636
7  class = 7101


row =

        8658

Row: 8658, pDepth = 1, loss = 0.090909

Decision tree for classification
1  if spectral_contrast_var_2<0.012531 then node 2 elseif spectral_contrast_var_2>=0.012531 then node 3 else 8274
2  class = 8445
3  class = 8274


row =

        5730

Row: 5730, pDepth = 1, loss = 0.074074

Decision tree for classification
1  if mfcc_dmean_0<0.137688 then node 2 elseif mfcc_dmean_0>=0.137688 then node 3 else 4773
2  class = 2875
3  class = 4773


row =

        8038

Row: 8038, pDepth = 1, loss = 0.027778

Decision tree for classification
1  if silence_rate_60dB_dvar2<0.184306 then node 2 elseif silence_rate_60dB_dvar2>=0.184306 then node 3 else 7209
2  class = 7209
3  class = 6792


row =

        8740

Row: 8740, pDepth = 2, loss = 0.181818

Decision tree for classification
1  if frequency_bands_dmean2_4<0.000412 then node 2 elseif frequency_bands_dmean2_4>=0.000412 then node 3 else 8496
2  class = 8496
3  if spectral_energyband_high_dvar2<0.002833 then node 4 elseif spectral_energyband_high_dvar2>=0.002833 then node 5 else 8560
4  if frequency_bands_dmean2_4<0.0095645 then node 6 elseif frequency_bands_dmean2_4>=0.0095645 then node 7 else 8560
5  class = 8496
6  if barkbands_dvar2_7<5.35e-05 then node 8 elseif barkbands_dvar2_7>=5.35e-05 then node 9 else 8560
7  class = 8560
8  class = 8560
9  class = 8496


row =

        8758

Row: 8758, pDepth = 1, loss = 0.029412

Decision tree for classification
1  if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
2  class = 8655
3  class = 8159


row =

        7323

Row: 7323, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
2  class = 8655
3  class = 8159


row =

        7958

Row: 7958, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
2  class = 8655
3  class = 8159


row =

        8594

Row: 8594, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
2  class = 8655
3  class = 8159


row =

        8718

Row: 8718, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
2  class = 8655
3  class = 8159


row =

        8396

Row: 8396, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if pitch_centroid_mean<0.711223 then node 2 elseif pitch_centroid_mean>=0.711223 then node 3 else 8655
2  class = 8655
3  class = 8159


row =

        8689

Row: 8689, pDepth = 1, loss = 0.034483

Decision tree for classification
1  if gfcc_dmean2_3<0.318661 then node 2 elseif gfcc_dmean2_3>=0.318661 then node 3 else 8405
2  class = 8405
3  class = 8573


row =

        8671

Row: 8671, pDepth = 1, loss = 0.095238

Decision tree for classification
1  if silence_rate_60dB_mean<0.875215 then node 2 elseif silence_rate_60dB_mean>=0.875215 then node 3 else 8039
2  class = 8039
3  class = 7615


row =

        8694

Row: 8694, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if silence_rate_60dB_mean<0.875215 then node 2 elseif silence_rate_60dB_mean>=0.875215 then node 3 else 8039
2  class = 8039
3  class = 7615


row =

        8557

Row: 8557, pDepth = 3, loss = 0.150685

Decision tree for classification
1  if spectral_skewness_min<0.963354 then node 2 elseif spectral_skewness_min>=0.963354 then node 3 else 8093
2  class = 8344
3  class = 8093


row =

        8626

Row: 8626, pDepth = 1, loss = 0.076923

Decision tree for classification
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
2  class = 8355
3  class = 8422


row =

        8566

Row: 8566, pDepth = 1, loss = 0.045455

Decision tree for classification
1  if scvalleys_dvar_5<0.0120155 then node 2 elseif scvalleys_dvar_5>=0.0120155 then node 3 else 8415
2  class = 8030
3  class = 8415


row =

        8813

Row: 8813, pDepth = 1, loss = 0.080645

Decision tree for classification
1  if mfcc_max_0<0.625379 then node 2 elseif mfcc_max_0>=0.625379 then node 3 else 8627
2  class = 8627
3  class = 8653


row =

        8603

Row: 8603, pDepth = 1, loss = 0.030303

Decision tree for classification
1  if dissonance_dmean<0.036456 then node 2 elseif dissonance_dmean>=0.036456 then node 3 else 7897
2  class = 7555
3  class = 7897


row =

        8780

Row: 8780, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if dissonance_dmean<0.036456 then node 2 elseif dissonance_dmean>=0.036456 then node 3 else 7897
2  class = 7555
3  class = 7897


row =

        8789

Row: 8789, pDepth = 4, loss = 0.177083

Decision tree for classification
1  if spectral_entropy_dmean<0.0841085 then node 2 elseif spectral_entropy_dmean>=0.0841085 then node 3 else 8624
2  class = 8339
3  class = 8624


row =

        8842

Row: 8842, pDepth = 2, loss = 0.153374

Decision tree for classification
1  if scvalleys_min_0<0.437758 then node 2 elseif scvalleys_min_0>=0.437758 then node 3 else 8803
2  class = 8803
3  class = 8754


row =

        8656

Row: 8656, pDepth = 1, loss = 0.083333

Decision tree for classification
1  if gfcc_median_1<0.294408 then node 2 elseif gfcc_median_1>=0.294408 then node 3 else 8498
2  class = 7813
3  class = 8498


row =

        8667

Row: 8667, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if gfcc_median_1<0.294408 then node 2 elseif gfcc_median_1>=0.294408 then node 3 else 8498
2  class = 7813
3  class = 8498


row =

        8699

Row: 8699, pDepth = 1, loss = 0.019608

Decision tree for classification
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
2  class = 8577
3  class = 8544


row =

        8746

Row: 8746, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8577
3  class = 8544


row =

        8640

Row: 8640, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8577
3  class = 8544


row =

        8692

Row: 8692, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8577
3  class = 8544


row =

        8714

Row: 8714, pDepth = 1, loss = 0.030769

Decision tree for classification
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
2  class = 8507
3  class = 8261


row =

        8792

Row: 8792, pDepth = 2, loss = 0.140845

Decision tree for classification
 1  if frequency_bands_dmean2_17<0.0004845 then node 2 elseif frequency_bands_dmean2_17>=0.0004845 then node 3 else 8715
 2  if tristimulus_min_1<0.008642 then node 4 elseif tristimulus_min_1>=0.008642 then node 5 else 8715
 3  if frequency_bands_dmean2_17<0.001461 then node 6 elseif frequency_bands_dmean2_17>=0.001461 then node 7 else 8613
 4  if frequency_bands_dmean_16<0.000217 then node 8 elseif frequency_bands_dmean_16>=0.000217 then node 9 else 8715
 5  class = 8613
 6  if frequency_bands_dmean_16<0.0001365 then node 10 elseif frequency_bands_dmean_16>=0.0001365 then node 11 else 8613
 7  if frequency_bands_dmean_16<0.0025025 then node 12 elseif frequency_bands_dmean_16>=0.0025025 then node 13 else 8715
 8  class = 8715
 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
10  class = 8715
11  class = 8613
12  if frequency_bands_dmean_16<0.0011695 then node 16 elseif frequency_bands_dmean_16>=0.0011695 then node 17 else 8715
13  class = 8613
14  if tristimulus_min_1<0.000348 then node 18 elseif tristimulus_min_1>=0.000348 then node 19 else 8613
15  if frequency_bands_dmean_16<0.0002655 then node 20 elseif frequency_bands_dmean_16>=0.0002655 then node 21 else 8715
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
17  class = 8715
18  if frequency_bands_dmean2_17<0.000276 then node 24 elseif frequency_bands_dmean2_17>=0.000276 then node 25 else 8613
19  class = 8715
20  class = 8613
21  class = 8715
22  if frequency_bands_dmean2_17<0.0113015 then node 26 elseif frequency_bands_dmean2_17>=0.0113015 then node 27 else 8715
23  class = 8715
24  if frequency_bands_dmean_16<0.000512 then node 28 elseif frequency_bands_dmean_16>=0.000512 then node 29 else 8715
25  class = 8613
26  if frequency_bands_dmean2_17<0.0050595 then node 30 elseif frequency_bands_dmean2_17>=0.0050595 then node 31 else 8613
27  class = 8715
28  class = 8613
29  class = 8715
30  class = 8715
31  class = 8613


row =

        8378

Row: 8378, pDepth = 1, loss = 0.108108

Decision tree for classification
1  if first_peak_spread_min<0.099624 then node 2 elseif first_peak_spread_min>=0.099624 then node 3 else 8129
2  class = 8129
3  class = 7757


row =

        8531

Row: 8531, pDepth = 1, loss = 0.093023

Decision tree for classification
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
2  class = 7554
3  class = 8476


row =

        8604

Row: 8604, pDepth = 1, loss = 0.093750

Decision tree for classification
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
2  class = 8225
3  class = 7956


row =

        8702

Row: 8702, pDepth = 3, loss = 0.130952

Decision tree for classification
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
2  class = 8466
3  class = 8340


row =

        8386

Row: 8386, pDepth = 2, loss = 0.125000

Decision tree for classification
1  if scvalleys_median_2<0.744365 then node 2 elseif scvalleys_median_2>=0.744365 then node 3 else 8182
2  class = 8182
3  class = 7455


row =

        8862

Row: 8862, pDepth = 3, loss = 0.118343

Decision tree for classification
1  if spectral_flux_median<0.020283 then node 2 elseif spectral_flux_median>=0.020283 then node 3 else 8701
2  if spectral_skewness_median<0.0641445 then node 4 elseif spectral_skewness_median>=0.0641445 then node 5 else 8701
3  class = 8802
4  class = 8701
5  class = 8802


row =

        8737

Row: 8737, pDepth = 2, loss = 0.132530

Decision tree for classification
1  if spectral_rms_max<0.181331 then node 2 elseif spectral_rms_max>=0.181331 then node 3 else 8681
2  class = 8681
3  class = 8372


row =

        8899

Row: 8899, pDepth = 1, loss = 0.022727

Decision tree for classification
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
2  class = 8847
3  class = 8827


row =

        7673

Row: 7673, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8847
3  class = 8827


row =

        8361

Row: 8361, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8847
3  class = 8827


row =

        8190

Row: 8190, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8847
3  class = 8827


row =

        8417

Row: 8417, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8847
3  class = 8827


row =

        8433

Row: 8433, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8847
3  class = 8827


row =

        8700

Row: 8700, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8847
3  class = 8827


row =

        8610

Row: 8610, pDepth = 1, loss = 0.064516

Decision tree for classification
1  if frequency_bands_dvar_1<0.001967 then node 2 elseif frequency_bands_dvar_1>=0.001967 then node 3 else 8407
2  class = 7899
3  class = 8407


row =

        8666

Row: 8666, pDepth = 1, loss = 0.047619

Decision tree for classification
1  if erb_bands_median_1<5e-07 then node 2 elseif erb_bands_median_1>=5e-07 then node 3 else 8314
2  class = 7766
3  class = 8314


row =

        8089

Row: 8089, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if erb_bands_median_1<5e-07 then node 2 elseif erb_bands_median_1>=5e-07 then node 3 else 8314
2  class = 7766
3  class = 8314


row =

        8782

Row: 8782, pDepth = 1, loss = 0.062500

Decision tree for classification
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
2  class = 8695
3  class = 8556


row =

        8709

Row: 8709, pDepth = 1, loss = 0.095238

Decision tree for classification
1  if first_peak_weight_min<0.816666 then node 2 elseif first_peak_weight_min>=0.816666 then node 3 else 8518
2  class = 8413
3  class = 8518


row =

        8796

Row: 8796, pDepth = 3, loss = 0.172727

Decision tree for classification
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
2  class = 8756
3  class = 8636


row =

        8810

Row: 8810, pDepth = 2, loss = 0.103448

Decision tree for classification
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
2  class = 8638
3  class = 8631


row =

        8878

Row: 8878, pDepth = 1, loss = 0.044776

Decision tree for classification
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
2  class = 8732
3  class = 8855


row =

        7450

Row: 7450, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8732
3  class = 8855


row =

        8917

Row: 8917, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8732
3  class = 8855


row =

        8590

Row: 8590, pDepth = 1, loss = 0.018868

Decision tree for classification
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
2  class = 7699
3  class = 7867


row =

        8648

Row: 8648, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 7699
3  class = 7867


row =

        8596

Row: 8596, pDepth = 1, loss = 0.113208

Decision tree for classification
1  if logattacktime_max<0.590546 then node 2 elseif logattacktime_max>=0.590546 then node 3 else 8328
2  class = 8328
3  class = 8133


row =

        8642

Row: 8642, pDepth = 1, loss = 0.049587

Decision tree for classification
1  if silence_rate_30dB_dvar2<0.015404 then node 2 elseif silence_rate_30dB_dvar2>=0.015404 then node 3 else 8343
2  class = 8343
3  class = 8559


row =

        8673

Row: 8673, pDepth = 1, loss = 0.102041

Decision tree for classification
1  if scvalleys_max_1<0.593168 then node 2 elseif scvalleys_max_1>=0.593168 then node 3 else 8428
2  class = 8234
3  class = 8428


row =

        8713

Row: 8713, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_max_1<0.593168 then node 2 elseif scvalleys_max_1>=0.593168 then node 3 else 8428
2  class = 8234
3  class = 8428


row =

        8742

Row: 8742, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if scvalleys_max_1<0.593168 then node 2 elseif scvalleys_max_1>=0.593168 then node 3 else 8428
2  class = 8234
3  class = 8428


row =

        8851

Row: 8851, pDepth = 5, loss = 0.162963

Decision tree for classification
1  if spectral_flatness_db_dvar<0.022638 then node 2 elseif spectral_flatness_db_dvar>=0.022638 then node 3 else 8717
2  class = 8580
3  class = 8717


row =

        8458

Row: 8458, pDepth = 1, loss = 0.105263

Decision tree for classification
1  if spectral_entropy_var<0.008238 then node 2 elseif spectral_entropy_var>=0.008238 then node 3 else 8104
2  class = 7759
3  class = 8104


row =

        8618

Row: 8618, pDepth = 2, loss = 0.109091

Decision tree for classification
1  if zerocrossingrate_max<0.495847 then node 2 elseif zerocrossingrate_max>=0.495847 then node 3 else 8545
2  class = 7886
3  class = 8545


row =

        8817

Row: 8817, pDepth = 2, loss = 0.058824

Decision tree for classification
1  if scvalleys_min_1<0.063374 then node 2 elseif scvalleys_min_1>=0.063374 then node 3 else 8728
2  class = 8649
3  class = 8728


row =

        8830

Row: 8830, pDepth = 4, loss = 0.154545

Decision tree for classification
1  if spectral_decrease_mean<0.89364 then node 2 elseif spectral_decrease_mean>=0.89364 then node 3 else 8674
2  class = 8646
3  class = 8674


row =

        8521

Row: 8521, pDepth = 2, loss = 0.096154

Decision tree for classification
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
2  class = 7543
3  class = 8079


row =

        8800

Row: 8800, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 7543
3  class = 8079


row =

        8845

Row: 8845, pDepth = 3, loss = 0.120253

Decision tree for classification
1  if spectral_energyband_high_max<0.001368 then node 2 elseif spectral_energyband_high_max>=0.001368 then node 3 else 8825
2  class = 8825
3  class = 8704


row =

        8889

Row: 8889, pDepth = 1, loss = 0.027322

Decision tree for classification
1  if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
2  class = 8779
3  class = 8749


row =

        6804

Row: 6804, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
2  class = 8779
3  class = 8749


row =

        7906

Row: 7906, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
2  class = 8779
3  class = 8749


row =

        6948

Row: 6948, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
2  class = 8779
3  class = 8749


row =

        8009

Row: 8009, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
2  class = 8779
3  class = 8749


row =

        7066

Row: 7066, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
2  class = 8779
3  class = 8749


row =

        7381

Row: 7381, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
2  class = 8779
3  class = 8749


row =

        6675

Row: 6675, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if first_peak_spread_max<0.431704 then node 2 elseif first_peak_spread_max>=0.431704 then node 3 else 8779
2  class = 8779
3  class = 8749


row =

        7995

Row: 7995, pDepth = 1, loss = 0.047619

Decision tree for classification
1  if spectral_flux_dmean<0.007513 then node 2 elseif spectral_flux_dmean>=0.007513 then node 3 else 7083
2  class = 7083
3  class = 7681


row =

        8669

Row: 8669, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_flux_dmean<0.007513 then node 2 elseif spectral_flux_dmean>=0.007513 then node 3 else 7083
2  class = 7083
3  class = 7681


row =

        8820

Row: 8820, pDepth = 2, loss = 0.169014

Decision tree for classification
1  if effective_duration_min<0.103585 then node 2 elseif effective_duration_min>=0.103585 then node 3 else 8797
2  class = 8680
3  class = 8797


row =

        8795

Row: 8795, pDepth = 2, loss = 0.100000

Decision tree for classification
1  if spectral_energy_var<0.0035125 then node 2 elseif spectral_energy_var>=0.0035125 then node 3 else 8762
2  class = 8762
3  class = 8558


row =

        8859

Row: 8859, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_energy_var<0.0035125 then node 2 elseif spectral_energy_var>=0.0035125 then node 3 else 8762
2  class = 8762
3  class = 8558


row =

        8834

Row: 8834, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_energy_var<0.0035125 then node 2 elseif spectral_energy_var>=0.0035125 then node 3 else 8762
2  class = 8762
3  class = 8558


row =

        8844

Row: 8844, pDepth = 3, loss = 0.180180

Decision tree for classification
1  if first_peak_spread_min<0.069737 then node 2 elseif first_peak_spread_min>=0.069737 then node 3 else 8676
2  class = 8676
3  class = 8647


row =

        8849

Row: 8849, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if first_peak_spread_min<0.069737 then node 2 elseif first_peak_spread_min>=0.069737 then node 3 else 8676
2  class = 8676
3  class = 8647


row =

        8853

Row: 8853, pDepth = 1, loss = 0.060000

Decision tree for classification
1  if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
2  class = 8597
3  class = 8747


row =

        6761

Row: 6761, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
2  class = 8597
3  class = 8747


row =

        6868

Row: 6868, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
2  class = 8597
3  class = 8747


row =

        7824

Row: 7824, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
2  class = 8597
3  class = 8747


row =

        6110

Row: 6110, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
2  class = 8597
3  class = 8747


row =

        7223

Row: 7223, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
2  class = 8597
3  class = 8747


row =

        8172

Row: 8172, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_crest_mean<0.481765 then node 2 elseif spectral_crest_mean>=0.481765 then node 3 else 8597
2  class = 8597
3  class = 8747


row =

        8539

Row: 8539, pDepth = 1, loss = 0.027778

Decision tree for classification
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
2  class = 7930
3  class = 8388


row =

        4617

Row: 4617, pDepth = 0, loss = 1.000000

Decision tree for classification
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
2  class = 7930
3  class = 8388


row =

        7161

Row: 7161, pDepth = 0, loss = 1.000000

Decision tree for classification
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
2  class = 7930
3  class = 8388


row =

        8438

Row: 8438, pDepth = 0, loss = 1.000000

Decision tree for classification
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
2  class = 7930
3  class = 8388


row =

        8526

Row: 8526, pDepth = 0, loss = 1.000000

Decision tree for classification
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
2  class = 7930
3  class = 8388


row =

        8486

Row: 8486, pDepth = 0, loss = 1.000000

Decision tree for classification
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
2  class = 7930
3  class = 8388


row =

        8729

Row: 8729, pDepth = 0, loss = 1.000000

Decision tree for classification
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
2  class = 7930
3  class = 8388


row =

        8687

Row: 8687, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 7930
3  class = 8388


row =

        8806

Row: 8806, pDepth = 1, loss = 0.082353

Decision tree for classification
1  if silence_rate_20dB_dmean<0.017467 then node 2 elseif silence_rate_20dB_dmean>=0.017467 then node 3 else 8668
2  class = 8668
3  class = 8609


row =

        8600

Row: 8600, pDepth = 0, loss = 1.000000

Decision tree for classification
1  if silence_rate_20dB_dmean<0.017467 then node 2 elseif silence_rate_20dB_dmean>=0.017467 then node 3 else 8668
2  class = 8668
3  class = 8609


row =

        8866

Row: 8866, pDepth = 2, loss = 0.150000

Decision tree for classification
1  if barkbands_var_11<0.000645 then node 2 elseif barkbands_var_11>=0.000645 then node 3 else 8809
2  class = 8809
3  if frequency_bands_max_8<0.0328895 then node 4 elseif frequency_bands_max_8>=0.0328895 then node 5 else 8839
4  class = 8839
5  if barkbands_max_21<1.9e-05 then node 6 elseif barkbands_max_21>=1.9e-05 then node 7 else 8809
6  class = 8839
7  class = 8809


row =

        7749

Row: 7749, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if barkbands_var_11<0.000645 then node 2 elseif barkbands_var_11>=0.000645 then node 3 else 8809
2  class = 8809
3  if frequency_bands_max_8<0.0328895 then node 4 elseif frequency_bands_max_8>=0.0328895 then node 5 else 8839
4  class = 8839
5  if barkbands_max_21<1.9e-05 then node 6 elseif barkbands_max_21>=1.9e-05 then node 7 else 8809
6  class = 8839
7  class = 8809


row =

        8736

Row: 8736, pDepth = 1, loss = 0.056604

Decision tree for classification
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
2  class = 8691
3  class = 8416


row =

        8805

Row: 8805, pDepth = 1, loss = 0.057971

Decision tree for classification
1  if spectral_contrast_max_1<0.574351 then node 2 elseif spectral_contrast_max_1>=0.574351 then node 3 else 8684
2  class = 8550
3  class = 8684


row =

        8874

Row: 8874, pDepth = 4, loss = 0.122807

Decision tree for classification
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
2  class = 8798
3  class = 8770


row =

        8654

Row: 8654, pDepth = 1, loss = 1.000000

Decision tree for classification
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
2  class = 8798
3  class = 8770


row =

        8706

Row: 8706, pDepth = 1, loss = 0.044444

Decision tree for classification
1  if spectral_contrast_max_1<0.641064 then node 2 elseif spectral_contrast_max_1>=0.641064 then node 3 else 8504
2  class = 8504
3  class = 8192


row =

        8822

Row: 8822, pDepth = 1, loss = 0.063291

Decision tree for classification
1  if inharmonicity_mean<0.0065225 then node 2 elseif inharmonicity_mean>=0.0065225 then node 3 else 8763
2  class = 8651
3  class = 8763


row =

        8887

Row: 8887, pDepth = 6, loss = 0.137566

Decision tree for classification
1  if spectral_centroid_var<0.0022275 then node 2 elseif spectral_centroid_var>=0.0022275 then node 3 else 8818
2  class = 8685
3  class = 8818


row =

        8593

Row: 8593, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_centroid_var<0.0022275 then node 2 elseif spectral_centroid_var>=0.0022275 then node 3 else 8818
2  class = 8685
3  class = 8818


row =

        8793

Row: 8793, pDepth = 1, loss = 1.000000

Decision tree for classification
1  if spectral_centroid_var<0.0022275 then node 2 elseif spectral_centroid_var>=0.0022275 then node 3 else 8818
2  class = 8685
3  class = 8818


row =

        8867

Row: 8867, pDepth = 1, loss = 0.047619

Decision tree for classification
1  if gfcc_mean_0<0.826244 then node 2 elseif gfcc_mean_0>=0.826244 then node 3 else 8751
2  class = 8751
3  class = 8814


row =

        8907

Row: 8907, pDepth = 3, loss = 0.106280

Decision tree for classification
1  if spectral_contrast_max_4<0.46882 then node 2 elseif spectral_contrast_max_4>=0.46882 then node 3 else 8892
2  class = 8892
3  class = 8787


row =

        7000


row =

        6920


row =

        7265


row =

        7536


row =

        6798


row =

        7374


row =

        4902


row =

        6179


row =

        8193


row =

        7938


row =

        8055


row =

        8023


row =

        8281


row =

        6958


row =

        7244


row =

        7873


row =

        7796


row =

        8034


row =

        8350


row =

        7936


row =

        8411


row =

        5805


row =

        6364


row =

        6233


row =

        7341


row =

        8080


row =

        1987


row =

        6722


row =

        7116


row =

        7388


row =

        7674


row =

        6367


row =

        8135


row =

        7974


row =

        8356


row =

        7961


row =

        8607


row =

        8571


row =

        8639


row =

        8505


row =

        8766


row =

        8663


row =

        8705


row =

        7946


row =

        8228


row =

        7558


row =

        8186


row =

        5006


row =

        6126


row =

        6974


row =

        8352


row =

        8303


row =

        8643


row =

        8620


row =

        8778


row =

        8576


row =

        8591


row =

        8625


row =

        8733


row =

        7703


row =

        8436


row =

        8452


row =

        8664


row =

        8091


row =

        8219


row =

        8522


row =

        8547


row =

        8511


row =

        8575


row =

        8524


row =

        8326


row =

        8561


row =

        8439


row =

        8461


row =

        2361


row =

        4119


row =

        5299


row =

        6338


row =

        6445


row =

        1901


row =

        5174


row =

        6530


row =

        6766


row =

        5504


row =

        7581


row =

        4035


row =

        6376


row =

        4761


row =

        4901


row =

        5111


row =

        5235


row =

        6597


row =

        7428


row =

        6652


row =

        6934


row =

        7035


row =

        7266


row =

        7200


row =

        7770


row =

        6283


row =

        7875


row =

        2898


row =

        4236


row =

        3680


row =

        4806


row =

   408


row =

        1764


row =

        6500


row =

        6997


row =

        5716


row =

        6307


row =

        6572


row =

        7451


row =

        7695


row =

        8058


row =

        5497


row =

        6565


row =

        5891


row =

        7864


row =

        8292


row =

        8252


row =

        7464


row =

        7626


row =

        6713


row =

        7971


row =

        6905


row =

        7317


row =

        7377


row =

        8070


row =

        4199


row =

        6035


row =

        5745


row =

        6282


row =

        5878


row =

        6292


row =

        7671


row =

        8410


row =

        7677


row =

        8268


row =

        8455


row =

        8599


row =

        8444


row =

        8481


row =

        5364


row =

        5588


row =

   975


row =

        5963


row =

        4818


row =

        5569


row =

        7495


row =

        7539


row =

        7683


row =

        8160


row =

        8078


row =

        8269


row =

        7753


row =

        7954


row =

        8499


row =

        8509


row =

        7501


row =

        7647


row =

        7403


row =

        7982


row =

        1976


row =

        6207


row =

        7832


row =

        7942


row =

        1267


row =

        5934


row =

        6854


row =

        7127


row =

        7668


row =

        8122


row =

        7750


row =

        8353


row =

        7798


row =

        7917


row =

        7551


row =

        8094


row =

        6636


row =

        7101


row =

        8274


row =

        8445


row =

        2875


row =

        4773


row =

        6792


row =

        7209


row =

        8496


row =

        8560


row =

        8159


row =

        8655


row =

        4991


row =

        7207


row =

        7274


row =

        7837


row =

        8120


row =

        8385


row =

        7002


row =

        8532


row =

        7872


row =

        8165


row =

        8405


row =

        8573


row =

        7615


row =

        8039


row =

        8297


row =

        8349


row =

        8093


row =

        8344


row =

        8355


row =

        8422


row =

        8030


row =

        8415


row =

        8627


row =

        8653


row =

        7555


row =

        7897


row =

        8440


row =

        8541


row =

        8339


row =

        8624


row =

        8754


row =

        8803


row =

        7813


row =

        8498


row =

        7967


row =

        8351


row =

        8544


row =

        8577


row =

        7593


row =

        8536


row =

        8357


row =

        8448


row =

        8068


row =

        8523


row =

        8261


row =

        8507


row =

        8613


row =

        8715


row =

        7757


row =

        8129


row =

        7554


row =

        8476


row =

        7956


row =

        8225


row =

        8340


row =

        8466


row =

        7455


row =

        8182


row =

        8701


row =

        8802


row =

        8372


row =

        8681


row =

        8827


row =

        8847


row =

        5391


row =

        6286


row =

        7176


row =

        7933


row =

        6836


row =

        7794


row =

        8041


row =

        8162


row =

        7975


row =

        8101


row =

        8278


row =

        8565


row =

        7899


row =

        8407


row =

        7766


row =

        8314


row =

        7158


row =

        8556


row =

        8695


row =

        8413


row =

        8518


row =

        8636


row =

        8756


row =

        8631


row =

        8638


row =

        8732


row =

        8855


row =

        6316


row =

        6833


row =

        8836


row =

        8877


row =

        7699


row =

        7867


row =

        8242


row =

        8478


row =

        8133


row =

        8328


row =

        8343


row =

        8559


row =

        8234


row =

        8428


row =

        8026


row =

        8582


row =

        6507


row =

        8634


row =

        8580


row =

        8717


row =

        7759


row =

        8104


row =

        7886


row =

        8545


row =

        8649


row =

        8728


row =

        8646


row =

        8674


row =

        7543


row =

        8079


row =

        8298


row =

        8503


row =

        8704


row =

        8825


row =

        8749


row =

        8779


row =

        4206


row =

        5023


row =

        6321


row =

        7205


row =

   212


row =

        4207


row =

        7043


row =

        7316


row =

        2939


row =

        4764


row =

        5856


row =

        6814


row =

        3520


row =

        5353


row =

        7083


row =

        7681


row =

        8475


row =

        8564


row =

        8680


row =

        8797


row =

        8558


row =

        8762


row =

        8672


row =

        8757


row =

        8752


row =

        8776


row =

        8647


row =

        8676


row =

        8570


row =

        8753


row =

        8597


row =

        8747


row =

        4829


row =

        5765


row =

        1064


row =

        7601


row =

        2998


row =

        5736


row =

        6396


row =

        4960


row =

        7421


row =

        7930


row =

        8388


row =

        3010


row =

        8029


row =

        8238


row =

        4631


row =

        7459


row =

        7519


row =

        8206


row =

        7592


row =

        8470


row =

        8115


row =

        8495


row =

        8609


row =

        8668


row =

        1035


row =

        3893


row =

        8809


row =

        8839


row =

        4664


row =

        4723


row =

        8416


row =

        8691


row =

        8550


row =

        8684


row =

        8770


row =

        8798


row =

        8406


row =

        8578


row =

        8192


row =

        8504


row =

        8651


row =

        8763


row =

        8685


row =

        8818


row =

        8149


row =

        8402


row =

        8454


row =

        8527


row =

        8751


row =

        8814


row =

        8787


row =

        8892