Add 4 Hz modulation feature

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)

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):
+def extractFeatures(audio, outputPath, sampleRate):
-	# Loads the audio file specified
+	if isinstance(audio, str):
-	loader = MonoLoader(filename = audioPath, sampleRate = sampleRate)
+		# Loads the audio file specified
-	audio = loader()
+		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)
 

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, 3.22619644066e-06, 3.12461406793e-06, 3.25462428918e-06, 3.16482783091e-06, 3.21278912452e-06, 2.84790849037e-06, 2.53104849435e-06, 2.33471928368e-06, 1.61461775861e-06, 1.60698527907e-06, 1.91222261492e-06, 1.69064901456e-06, 1.53823145865e-06, 1.9076562694e-06, 1.55232464749e-06, 1.84713394447e-06, 1.66909092059e-06, 2.3762750061e-06, 2.21101390707e-06, 2.68857161245e-06, 2.30758610087e-06, 2.61445188698e-06, 2.73767045655e-06, 2.51227879744e-06, 2.92989739137e-06, 3.22595815305e-06, 2.99051316688e-06, 1.51773735979e-06, 1.77224035269e-06, 2.03530157705e-06, 2.38050756707e-06, 2.01225816454e-06, 2.04659841074e-06, 2.43184422288e-06, 2.60434512711e-06, 3.0137136946e-06, 3.23982317241e-06, 3.355549552e-06, 3.38863242177e-06, 2.43408953793e-06, 2.17560273086e-06, 1.91663548321e-06, 2.75783372672e-06, 2.89882927973e-06, 3.17417993756e-06, 2.30382556765e-06, 2.37212339016e-06, 2.7921905712e-06, 3.06454489873e-06, 3.40504766427e-06, 4.11734163208e-06, 3.99186046707e-06, 3.64837796951e-06, 3.26973031406e-06, 2.9940249533e-06, 3.2533976082e-06, 3.46150977748e-06, 2.98416398437e-06, 3.60520425602e-06, 2.948436304e-06, 3.22521395901e-06, 2.97287306239e-06, 2.80955487142e-06, 2.67753284788e-06, 2.52599124906e-06, 2.26963629757e-06, 2.76785772257e-06, 2.66778215519e-06, 2.73703312814e-06, 2.70789610113e-06, 3.31624164573e-06, 2.62994421973e-06, 2.50126890933e-06, 2.87566444968e-06, 2.84614452539e-06, 3.17297303809e-06, 3.32110084855e-06, 3.67763141185e-06, 4.03447529607e-06, 2.87972170554e-06, 3.26251529259e-06, 2.80281528831e-06, 2.87941293209e-06, 2.56147882283e-06, 2.92708818961e-06, 2.97309384223e-06, 2.90167872663e-06, 3.04149966723e-06, 2.94663686873e-06, 2.84449424726e-06, 3.28026294483e-06, 3.12918041345e-06, 3.132476877e-06, 2.9896855267e-06, 3.20049434777e-06, 2.51384199146e-06, 3.10284690386e-06, 2.42540295403e-06, 2.58964519162e-06, 2.59849753093e-06, 2.39842233896e-06, 3.96386849388e-06, 2.82618134406e-06, 3.34821083925e-06, 2.78872084891e-06, 2.30860359807e-06, 2.85347732643e-06, 3.13947793984e-06, 3.34703531735e-06, 3.30236343871e-06, 3.41068403031e-06, 3.52642678081e-06, 2.90016168947e-06, 2.80370045402e-06, 2.61742707153e-06, 2.94969822789e-06, 3.65233699995e-06, 2.71426620202e-06, 2.92343565889e-06, 2.758428991e-06, 2.64599293587e-06, 3.61212073585e-06, 2.02746673494e-06, 1.97196345653e-06, 2.51555934483e-06, 2.300911774e-06, 2.28063208851e-06, 2.40202234636e-06, 2.50573793892e-06, 2.19754429054e-06, 1.8692184085e-06, 2.21020627578e-06, 2.43799809141e-06, 2.08589381145e-06, 2.52597055805e-06, 2.83864869743e-06, 2.59928128798e-06, 2.75564457297e-06, 2.53719326793e-06, 2.61828995463e-06, 2.72551164926e-06, 2.9514187645e-06, 2.1480441319e-06, 2.08600772567e-06, 2.15426121031e-06, 2.6441416594e-06, 2.42884425461e-06, 2.6728098419e-06, 2.05719402402e-06, 3.01643558487e-06, 2.13632256418e-06, 1.91376443581e-06, 2.54410633715e-06, 2.8410656796e-06, 3.09688607558e-06, 2.39751216213e-06, 2.64523714577e-06, 2.49900381277e-06, 2.92071126751e-06, 3.05252797261e-06, 2.66938809546e-06, 2.43183967541e-06, 3.07734444505e-06, 3.08140010929e-06, 2.92793811241e-06, 3.39578900821e-06, 3.38618428941e-06, 2.75860566035e-06, 2.88854516839e-06, 2.73512864624e-06, 2.84732391265e-06, 3.01290947391e-06, 3.06271908812e-06, 3.71076885131e-06, 3.81384620596e-06, 3.8165303522e-06, 2.79376922663e-06, 2.53163375419e-06, 2.85674354927e-06, 2.91990181722e-06, 2.02478213396e-06, 2.53031998909e-06, 3.15131615025e-06, 3.28783266923e-06, 3.28259170601e-06, 3.40505403074e-06, 3.00376927953e-06, 2.54675342148e-06, 2.6826839985e-06, 3.20988851854e-06, 3.01445584228e-06, 2.9969901334e-06, 3.1681395285e-06, 3.0691558095e-06, 3.57189833267e-06, 3.38160907631e-06, 2.96983193948e-06, 3.17814033224e-06, 3.59006412509e-06, 2.65751577899e-06, 3.03901197185e-06, 2.95159429697e-06, 2.63419337898e-06, 2.60912065642e-06, 2.76196101368e-06, 3.44932550433e-06],
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classifier/feature_extraction/music_features/ballad.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/music_features/bartok.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/music_features/beat.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/music_features/beatles.json

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classifier/feature_extraction/music_features/bigband.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/music_features/birdland.json

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classifier/feature_extraction/music_features/blues.json

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classifier/feature_extraction/music_features/bmarsalis.json

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classifier/feature_extraction/music_features/brahms.json

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classifier/feature_extraction/music_features/canonaki.json

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classifier/feature_extraction/music_features/caravan.json

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classifier/feature_extraction/music_features/chaka.json

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classifier/feature_extraction/music_features/classical.json

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classifier/feature_extraction/music_features/classical1.json

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classifier/feature_extraction/music_features/classical2.json

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classifier/feature_extraction/music_features/copland.json

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classifier/feature_extraction/music_features/copland2.json

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classifier/feature_extraction/music_features/corea.json

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classifier/feature_extraction/music_features/corea1.json

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classifier/feature_extraction/music_features/cure.json

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classifier/feature_extraction/music_features/debussy.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/music_features/deedee.json

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classifier/feature_extraction/music_features/deedee1.json

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classifier/feature_extraction/music_features/duke.json

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classifier/feature_extraction/music_features/echoes.json

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classifier/feature_extraction/music_features/eguitar.json

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classifier/feature_extraction/music_features/georose.json

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classifier/feature_extraction/music_features/gismonti.json

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classifier/feature_extraction/music_features/glass.json

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classifier/feature_extraction/music_features/glass1.json

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classifier/feature_extraction/music_features/gravity.json

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classifier/feature_extraction/music_features/gravity2.json

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classifier/feature_extraction/music_features/guitar.json

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classifier/feature_extraction/music_features/hendrix.json

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classifier/feature_extraction/music_features/ipanema.json

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classifier/feature_extraction/music_features/jazz.json

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classifier/feature_extraction/music_features/jazz1.json

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classifier/feature_extraction/music_features/led.json

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classifier/feature_extraction/music_features/loreena.json

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classifier/feature_extraction/music_features/madradeus.json

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classifier/feature_extraction/music_features/magkas.json

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classifier/feature_extraction/music_features/march.json

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classifier/feature_extraction/music_features/marlene.json

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classifier/feature_extraction/music_features/mingus.json

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classifier/feature_extraction/music_features/mingus1.json

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classifier/feature_extraction/music_features/misirlou.json

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classifier/feature_extraction/music_features/moanin.json

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classifier/feature_extraction/music_features/narch.json

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classifier/feature_extraction/music_features/ncherry.json

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classifier/feature_extraction/music_features/nearhou.json

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classifier/feature_extraction/music_features/opera.json

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classifier/feature_extraction/music_features/opera1.json

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classifier/feature_extraction/music_features/pop.json

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classifier/feature_extraction/music_features/prodigy.json

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classifier/feature_extraction/music_features/redhot.json

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classifier/feature_extraction/music_features/rock.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/music_features/rock2.json

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classifier/feature_extraction/music_features/russo.json

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classifier/feature_extraction/music_features/tony.json

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classifier/feature_extraction/music_features/u2.json

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classifier/feature_extraction/music_features/unpoco.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/music_features/vlobos.json

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classifier/feature_extraction/music_features/winds.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/speech_features/acomic.json

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classifier/feature_extraction/speech_features/acomic2.json

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classifier/feature_extraction/speech_features/allison.json

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classifier/feature_extraction/speech_features/amal.json

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classifier/feature_extraction/speech_features/austria.json

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classifier/feature_extraction/speech_features/bathroom1.json

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classifier/feature_extraction/speech_features/chant.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/speech_features/charles.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/speech_features/china.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/speech_features/comedy.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/speech_features/comedy1.json

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classifier/feature_extraction/speech_features/conversion.json

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classifier/feature_extraction/speech_features/danie.json

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classifier/feature_extraction/speech_features/danie1.json

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classifier/feature_extraction/speech_features/dialogue.json

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classifier/feature_extraction/speech_features/dialogue1.json

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classifier/feature_extraction/speech_features/dialogue2.json

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classifier/feature_extraction/speech_features/diamond.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/speech_features/ellhnika.json

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classifier/feature_extraction/speech_features/emil.json

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classifier/feature_extraction/speech_features/fem_rock.json

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classifier/feature_extraction/speech_features/female.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/speech_features/fire.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/speech_features/geography.json

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classifier/feature_extraction/speech_features/geography1.json

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classifier/feature_extraction/speech_features/georg.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/speech_features/god.json

@@ -1,4 +1,5 @@
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classifier/feature_extraction/speech_features/greek.json

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classifier/feature_extraction/speech_features/greek1.json

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classifier/feature_extraction/speech_features/india.json

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classifier/feature_extraction/speech_features/jony.json

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classifier/feature_extraction/speech_features/jvoice.json

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classifier/feature_extraction/speech_features/kedar.json

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classifier/feature_extraction/speech_features/kid.json

@@ -1,4 +1,5 @@
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