From 1756cc04cfd034953427a9d8a3b3bc2cfa060c92 Mon Sep 17 00:00:00 2001 From: Apostolof Date: Sun, 9 Dec 2018 17:41:13 +0200 Subject: [PATCH] Add 4 Hz modulation feature --- .../model_training.py | 13 +++- .../feature_extraction/feature_extractor.py | 30 ++++++++-- .../music_features/bagpipe.json | 1 + .../music_features/ballad.json | 1 + .../music_features/bartok.json | 1 + .../music_features/beat.json | 1 + .../music_features/beatles.json | 1 + .../music_features/bigband.json | 1 + .../music_features/birdland.json | 1 + .../music_features/blues.json | 1 + .../music_features/bmarsalis.json | 1 + .../music_features/brahms.json | 1 + .../music_features/canonaki.json | 1 + .../music_features/caravan.json | 1 + .../music_features/chaka.json | 1 + .../music_features/classical.json | 1 + .../music_features/classical1.json | 1 + .../music_features/classical2.json | 1 + .../music_features/copland.json | 1 + .../music_features/copland2.json | 1 + .../music_features/corea.json | 1 + .../music_features/corea1.json | 1 + .../music_features/cure.json | 1 + .../music_features/debussy.json | 1 + .../music_features/deedee.json | 1 + .../music_features/deedee1.json | 1 + .../music_features/duke.json | 1 + .../music_features/echoes.json | 1 + .../music_features/eguitar.json | 1 + .../music_features/georose.json | 1 + .../music_features/gismonti.json | 1 + .../music_features/glass.json | 1 + .../music_features/glass1.json | 1 + .../music_features/gravity.json | 1 + .../music_features/gravity2.json | 1 + .../music_features/guitar.json | 1 + .../music_features/hendrix.json | 1 + .../music_features/ipanema.json | 1 + .../music_features/jazz.json | 1 + .../music_features/jazz1.json | 1 + .../music_features/led.json | 1 + .../music_features/loreena.json | 1 + .../music_features/madradeus.json | 1 + .../music_features/magkas.json | 1 + .../music_features/march.json | 1 + .../music_features/marlene.json | 1 + .../music_features/mingus.json | 1 + .../music_features/mingus1.json | 1 + .../music_features/misirlou.json | 1 + .../music_features/moanin.json | 1 + .../music_features/narch.json | 1 + .../music_features/ncherry.json | 1 + .../music_features/nearhou.json | 1 + .../music_features/opera.json | 1 + .../music_features/opera1.json | 1 + .../music_features/pop.json | 1 + .../music_features/prodigy.json | 1 + .../music_features/redhot.json | 1 + .../music_features/rock.json | 1 + .../music_features/rock2.json | 1 + .../music_features/russo.json | 1 + .../music_features/tony.json | 1 + .../feature_extraction/music_features/u2.json | 1 + .../music_features/unpoco.json | 1 + .../music_features/vlobos.json | 1 + .../music_features/winds.json | 1 + .../speech_features/acomic.json | 1 + .../speech_features/acomic2.json | 1 + .../speech_features/allison.json | 1 + .../speech_features/amal.json | 1 + .../speech_features/austria.json | 1 + .../speech_features/bathroom1.json | 1 + .../speech_features/chant.json | 1 + .../speech_features/charles.json | 1 + .../speech_features/china.json | 1 + .../speech_features/comedy.json | 1 + .../speech_features/comedy1.json | 1 + .../speech_features/conversion.json | 1 + .../speech_features/danie.json | 1 + .../speech_features/danie1.json | 1 + .../speech_features/dialogue.json | 1 + .../speech_features/dialogue1.json | 1 + .../speech_features/dialogue2.json | 1 + .../speech_features/diamond.json | 1 + .../speech_features/ellhnika.json | 1 + .../speech_features/emil.json | 1 + .../speech_features/fem_rock.json | 1 + .../speech_features/female.json | 1 + .../speech_features/fire.json | 1 + .../speech_features/geography.json | 1 + .../speech_features/geography1.json | 1 + .../speech_features/georg.json | 1 + .../speech_features/god.json | 1 + .../speech_features/greek.json | 1 + .../speech_features/greek1.json | 1 + .../speech_features/india.json | 1 + .../speech_features/jony.json | 1 + .../speech_features/jvoice.json | 1 + .../speech_features/kedar.json | 1 + .../speech_features/kid.json | 1 + .../speech_features/lena.json | 1 + .../speech_features/male.json | 1 + .../speech_features/my_voice.json | 1 + .../speech_features/nether.json | 1 + .../speech_features/news1.json | 1 + .../speech_features/news2.json | 1 + .../speech_features/nj105.json | 1 + .../speech_features/nj105a.json | 1 + .../speech_features/oneday.json | 1 + .../speech_features/psychic.json | 1 + .../speech_features/pulp.json | 1 + .../speech_features/pulp1.json | 1 + .../speech_features/pulp2.json | 1 + .../speech_features/relation.json | 1 + .../speech_features/serbian.json | 1 + .../speech_features/shannon.json | 1 + .../speech_features/sleep.json | 1 + .../speech_features/smoke1.json | 1 + .../speech_features/smoking.json | 1 + .../speech_features/stupid.json | 1 + .../speech_features/teachers.json | 1 + .../speech_features/teachers1.json | 1 + .../speech_features/teachers2.json | 1 + .../speech_features/thlui.json | 1 + .../speech_features/undergrad.json | 1 + .../speech_features/vegetables.json | 1 + .../speech_features/vegetables1.json | 1 + .../speech_features/vegetables2.json | 1 + .../speech_features/voice.json | 1 + .../speech_features/voices.json | 1 + classifier/pipeline.py | 9 ++- classifier/preprocessing/dataset.npy | Bin 3287168 -> 3287168 bytes classifier/preprocessing/featureKeys.npy | Bin 752 -> 776 bytes classifier/test/accuracyWithoutFeature.py | 56 +++++++++--------- 134 files changed, 196 insertions(+), 40 deletions(-) diff --git a/classifier/classification_model_training/model_training.py b/classifier/classification_model_training/model_training.py index c506012..c0f54b0 100644 --- a/classifier/classification_model_training/model_training.py +++ b/classifier/classification_model_training/model_training.py @@ -65,6 +65,7 @@ def randomForest(dataset, target): def kFCrossValid(dataset, target, model = 'svm'): from sklearn.model_selection import cross_val_score from sklearn import metrics + from copy import deepcopy clf = None @@ -95,12 +96,22 @@ def kFCrossValid(dataset, target, model = 'svm'): from sklearn.model_selection import KFold kf = KFold(n_splits=5, shuffle=True, random_state=2) + maxAccuracy = 0 + bestClf = None + for k, (train_index, test_index) in enumerate(kf.split(dataset)): kTrainSet, kTestSet = dataset[train_index], dataset[test_index] kTrainTarget, kTestTarget = target[train_index], target[test_index] clf.fit(kTrainSet, kTrainTarget) - print("[fold {0}], score: {1:.2f}".format(k, 100*clf.score(kTestSet, kTestTarget))) + acc = clf.score(kTestSet, kTestTarget) + print("[fold {0}], score: {1:.2f}".format(k, 100*acc)) + + if acc > maxAccuracy: + maxAccuracy = acc + bestClf = deepcopy(clf) + + return bestClf # Prints a nice message to let the user know the module was imported print(bcolors.BLUE + 'model_training loaded' + bcolors.ENDC) diff --git a/classifier/feature_extraction/feature_extractor.py b/classifier/feature_extraction/feature_extractor.py index 2af6f5e..aaa8025 100644 --- a/classifier/feature_extraction/feature_extractor.py +++ b/classifier/feature_extraction/feature_extractor.py @@ -3,7 +3,7 @@ import essentia from essentia.standard import (MonoLoader, Windowing, Spectrum, MFCC, ZeroCrossingRate, SpectralCentroidTime, RollOff, Flux, Envelope, FlatnessSFX, LogAttackTime, StrongDecay, FlatnessDB, HFC, - SpectralComplexity, FrameGenerator, YamlOutput) + SpectralComplexity, Energy, FrameGenerator, YamlOutput) # Disable annoying info level logging essentia.log.infoActive = False @@ -15,10 +15,11 @@ class bcolors: RED = '\033[91m' ENDC = '\033[0m' -def extractFeatures(audioPath, outputPath, sampleRate): - # Loads the audio file specified - loader = MonoLoader(filename = audioPath, sampleRate = sampleRate) - audio = loader() +def extractFeatures(audio, outputPath, sampleRate): + if isinstance(audio, str): + # Loads the audio file specified + loader = MonoLoader(filename = audio, sampleRate = sampleRate) + audio = loader() # Sets up the functions that will be used # TODO check if zero phase windowing is something we might want @@ -37,6 +38,7 @@ def extractFeatures(audioPath, outputPath, sampleRate): flatDB = FlatnessDB() hfc = HFC(sampleRate = sampleRate) spcComp = SpectralComplexity(sampleRate = sampleRate, magnitudeThreshold = 2) + energy = Energy() # Creates a pool to collect the values of the features pool = essentia.Pool() @@ -67,7 +69,9 @@ def extractFeatures(audioPath, outputPath, sampleRate): # Computes cepstral features # Discards the bands - mfcc_coeffs = mfcc(frameSpectrum)[1] + melBandEnergies, mfcc_coeffs = mfcc(frameSpectrum) + + fHzMod = _4HzModulation(melBandEnergies, energy(frameSpectrum), sampleRate) # Adds the values to the pool pool.add('ZCR', frameZCR) @@ -88,8 +92,22 @@ def extractFeatures(audioPath, outputPath, sampleRate): for index, coef in enumerate(mfcc_coeffs): pool.add('mfcc' + str(index), coef) + pool.add('4HzMod', fHzMod) + YamlOutput(filename = outputPath, format = 'json', writeVersion = False)(pool) +def _4HzModulation(melEnergies, frameEnergy, sampleRate): + from scipy.signal import butter, sosfilt, sosfreqz + nyquist = 0.5 * sampleRate + lowCut = 3 / nyquist + highCut = 5 / nyquist + sos = butter(N = 2, Wn = [lowCut, highCut], analog = False, btype = 'band', + output = 'sos') + filtered = sosfilt(sos = sos, x = melEnergies) + + energySum = sum(filtered) + return energySum / frameEnergy + # Prints a nice message to let the user know the module was imported print(bcolors.BLUE + 'feature_extractor loaded' + bcolors.ENDC) diff --git a/classifier/feature_extraction/music_features/bagpipe.json b/classifier/feature_extraction/music_features/bagpipe.json index f5dca62..635b64c 100644 --- a/classifier/feature_extraction/music_features/bagpipe.json +++ b/classifier/feature_extraction/music_features/bagpipe.json @@ -1,4 +1,5 @@ { +"4HzMod": [2.48501987699e-06, 2.64822574536e-06, 2.86789941129e-06, 2.73117666438e-06, 2.27833106692e-06, 2.27552550314e-06, 2.97525934911e-06, 2.80326003121e-06, 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a/classifier/feature_extraction/music_features/nearhou.json b/classifier/feature_extraction/music_features/nearhou.json index 63e324c..e1fe0c9 100644 --- a/classifier/feature_extraction/music_features/nearhou.json +++ b/classifier/feature_extraction/music_features/nearhou.json @@ -1,4 +1,5 @@ { +"4HzMod": [7.44352018955e-06, 7.39455526855e-06, 7.55581822887e-06, 8.68269398779e-06, 8.04822502687e-06, 1.16820338008e-05, 9.34585750656e-06, 7.43819600757e-06, 5.91829029872e-06, 7.49260198063e-06, 5.72804947296e-06, 7.7896647781e-06, 7.16024260328e-06, 8.84672590473e-06, 7.23148195902e-06, 1.08640415419e-05, 9.35700154514e-06, 1.00015640783e-05, 9.59184399107e-06, 7.22562799638e-06, 8.50571632327e-06, 9.05654815142e-06, 1.05247809188e-05, 9.17233774089e-06, 1.05437511593e-05, 1.0061613466e-05, 8.55421785673e-06, 8.92393836693e-06, 1.09010252345e-05, 8.9673912953e-06, 5.84389908909e-06, 1.06831103039e-05, 9.87018029264e-06, 7.76977685746e-06, 9.53043945628e-06, 8.24631933938e-06, 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0.122494332492, 0.0580045357347, 0.0452154204249, 0.0842176899314, 0.0165532883257, 0.0804988667369], diff --git a/classifier/pipeline.py b/classifier/pipeline.py index 2fdf6eb..3fd8720 100644 --- a/classifier/pipeline.py +++ b/classifier/pipeline.py @@ -1,7 +1,11 @@ import numpy as np +from feature_extraction.batch_feature_extractor import batchExtract from preprocessing.data_preprocessing import createSingleFeaturesArray, standardization, PCA from classification_model_training.model_training import simpleTrain, kFCrossValid +batchExtract('../dataset/music_wav/', 'feature_extraction/music_features/', 22050) +batchExtract('../dataset/speech_wav/', 'feature_extraction/speech_features/', 22050) + dataset, target, featureKeys = createSingleFeaturesArray( 'feature_extraction/music_features/', 'feature_extraction/speech_features/') @@ -10,9 +14,4 @@ dataset = standardization(dataset) # dataset = PCA(dataset) print('Simple train accuracy achieved = ' + str(simpleTrain(dataset, target))) kFCrossValid(dataset, target, model = 'svm') -kFCrossValid(dataset, target, model = 'rndForest') - -dataset = PCA(dataset) -print('Simple train accuracy achieved = ' + str(simpleTrain(dataset, target))) -kFCrossValid(dataset, target, model = 'svm') kFCrossValid(dataset, target, model = 'rndForest') \ No newline at end of file diff --git a/classifier/preprocessing/dataset.npy b/classifier/preprocessing/dataset.npy index 67b84a716b7da43158a5197876f859212ab1976f..e10dde5b8f01013ce378ba696e4e85b949cb04d2 100644 GIT binary patch literal 3287168 zcmbT7_al|>|Ho~S%#5T+q@l7!<8`QbS0rhmT~@Rsk|HHD5}6s1k&2A0%InyB@4Y$p zInFuu_?+**@IAks`-l5F=f3ajdcCg4^Kl29y{LcAgqi6X(+fE>^QU*7$Z1N+9kDnh zCod&u@$iZ5lUomNJbYqi{(r}HZryuo&N%+m>eeH3#`oPy>dG2Q@=}Vbd*r2FO8tL6 z+-x^%w9Z=hpmFwvzYkSc@i|jz+v~<@V4qH;gQJXJkQYD^be)%Rytvn;`zyqq11 z`bEGNy2&5HXwdj^$@S!u4m5ddkQ10f!3-y-IGdv+l-c#L=JmHuRJ(t*>*rq*Sh(}N zx~({mikpQ7uW2$9f7o`KI&qKS)sL8JaCQY_Fg$c;AqB8Ouqc$mMEK~?8M%|W1uHt! 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