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import numpy as np
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from sys import path
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path.append('..')
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from feature_extraction.batch_feature_extractor import batchExtract
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from preprocessing.data_preprocessing import createSingleFeaturesArray, standardization
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from classification_model_training.model_training import simpleTrain
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batchExtract('../../dataset/music_wav/', '../feature_extraction/music_features/', 22050)
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batchExtract('../../dataset/speech_wav/', '../feature_extraction/speech_features/', 22050)
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dataset, target, featureKeys = createSingleFeaturesArray(
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'../feature_extraction/music_features/',
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'../feature_extraction/speech_features/')
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dataset = standardization(dataset)
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wholeAccuracy = simpleTrain(dataset, target, 'svm')
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print('Accuracy using whole dataset = ' + str(wholeAccuracy))
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damages = np.zeros(featureKeys.size)
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for index, key in enumerate(featureKeys):
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acc = simpleTrain(np.delete(dataset, index, axis=1), target, 'svm')
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damages[index] = 100*(wholeAccuracy-acc)
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print('Accuracy without ' + key + '\t= ' + str(acc) +
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',\tdamage\t= ' + "%.2f" % damages[index] + '%')
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# Accuracy using whole dataset = 0.951902893127681
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# Accuracy without 4HzMod = 0.9456968148215752, damage = 0.62%
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# Accuracy without Flat = 0.9523592224148946, damage = -0.05%
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# Accuracy without HFC = 0.9526330199872228, damage = -0.07%
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# Accuracy without LAtt = 0.9524504882723374, damage = -0.05%
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# Accuracy without SC = 0.9520854248425664, damage = -0.02%
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# Accuracy without SComp = 0.948160992972529, damage = 0.37%
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# Accuracy without SDec = 0.9520854248425664, damage = -0.02%
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# Accuracy without SEFlat = 0.9513552979830245, damage = 0.05%
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# Accuracy without SF = 0.9492561832618417, damage = 0.26%
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# Accuracy without SFlat = 0.9496212466916126, damage = 0.23%
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# Accuracy without SLAtt = 0.9498950442639409, damage = 0.20%
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# Accuracy without SR = 0.9523592224148946, damage = -0.05%
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# Accuracy without SSDec = 0.9519941589851236, damage = -0.01%
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# Accuracy without ZCR = 0.9500775759788264, damage = 0.18%
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# Accuracy without mfcc0 = 0.9502601076937118, damage = 0.16%
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# Accuracy without mfcc1 = 0.9510815004106964, damage = 0.08%
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# Accuracy without mfcc10 = 0.9503513735511545, damage = 0.16%
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# Accuracy without mfcc11 = 0.9492561832618417, damage = 0.26%
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# Accuracy without mfcc12 = 0.9482522588299717, damage = 0.37%
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# Accuracy without mfcc2 = 0.9446928903897052, damage = 0.72%
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# Accuracy without mfcc3 = 0.9465182075385599, damage = 0.54%
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# Accuracy without mfcc4 = 0.9470658026832162, damage = 0.48%
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# Accuracy without mfcc5 = 0.9463356758236744, damage = 0.56%
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# Accuracy without mfcc6 = 0.9452404855343616, damage = 0.67%
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# Accuracy without mfcc7 = 0.9462444099662316, damage = 0.57%
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# Accuracy without mfcc8 = 0.9490736515469563, damage = 0.28%
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# Accuracy without mfcc9 = 0.9472483343981016, damage = 0.47%
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