Mercurial > hg > sfx-subgrouping
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author | DaveM |
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date | Sat, 01 Apr 2017 17:03:14 +0100 |
parents | 6155f4e3d37c |
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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