Mercurial > hg > sfx-subgrouping
changeset 34:781ebde125cf
updating analysis script
| author | DaveM |
|---|---|
| date | Thu, 16 Mar 2017 11:33:01 +0000 |
| parents | 74d123779d3b |
| children | 6155f4e3d37c |
| files | analysis/AnalysisOutput.txt analysis/analysisWorkflow.m |
| diffstat | 2 files changed, 7912 insertions(+), 0 deletions(-) [+] |
line wrap: on
line diff
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/analysis/AnalysisOutput.txt Thu Mar 16 11:33:01 2017 +0000 @@ -0,0 +1,7911 @@ +{Undefined function or variable 'listSize'. + +Error in <a href="matlab:matlab.internal.language.introspective.errorDocCallback('analysisWorkflow', '/Volumes/Internal/Documents/sfx-subgrouping/analysis/analysisWorkflow.m', 3)" style="font-weight:bold">analysisWorkflow</a> (<a href="matlab: opentoline('/Volumes/Internal/Documents/sfx-subgrouping/analysis/analysisWorkflow.m',3,0)">line 3</a>) +currentRow = [2*listSize-1]; +} +load('/Volumes/Internal/Documents/sfx-subgrouping/code/adobeDataNorm.mat') +if system_dependent('IsDebugMode')==1, dbquit; end +save('AdobeAllResults.mat') +analysisWorkflow +{Undefined function or variable 'listSize'. + +Error in <a href="matlab:matlab.internal.language.introspective.errorDocCallback('analysisWorkflow', '/Volumes/Internal/Documents/sfx-subgrouping/analysis/analysisWorkflow.m', 3)" style="font-weight:bold">analysisWorkflow</a> (<a href="matlab: opentoline('/Volumes/Internal/Documents/sfx-subgrouping/analysis/analysisWorkflow.m',3,0)">line 3</a>) +currentRow = [2*listSize-1]; +} +analysisWorkflow +{Undefined function or variable 'data'. + +Error in <a href="matlab:matlab.internal.language.introspective.errorDocCallback('analysisWorkflow', '/Volumes/Internal/Documents/sfx-subgrouping/analysis/analysisWorkflow.m', 3)" style="font-weight:bold">analysisWorkflow</a> (<a href="matlab: opentoline('/Volumes/Internal/Documents/sfx-subgrouping/analysis/analysisWorkflow.m',3,0)">line 3</a>) +listSize = size(data,1); +} +<a href="matlab: opentoline('/Volumes/Internal/Documents/sfx-subgrouping/analysis/analysisWorkflow.m',3,1)">3 </a>listSize = size(data,1); +if system_dependent('IsDebugMode')==1, dbquit; end +save('AdobeAllResults.mat') +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 = + + 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