add audio classifier

audioClassification/createClassifierModel.py

@@ -0,0 +1,17 @@
+#!/usr/local/bin/python2
+from pyAudioAnalysis import audioTrainTest as aT
+import os
+from sys import argv
+import pydub
+
+pydub.AudioSegment.converter = r"/Users/navrajnarula/Desktop/ffmpeg"
+
+script, dirname = argv
+
+subdirectories = os.listdir(dirname)
+subdirectories.pop(0)
+
+subdirectories = [dirname + "/" + subDirName for subDirName in subdirectories]
+
+print(subdirectories)
+aT.featureAndTrain(subdirectories, 1.0, 1.0, aT.shortTermWindow, aT.shortTermStep, "svm", "svmModel", False)

audioClassification/testClassifierModel.py

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+#!/usr/local/bin/python2
+from sys import argv
+import numpy as np
+from pyAudioAnalysis import audioTrainTest as aT
+
+import pydub
+
+pydub.AudioSegment.converter = r"/Users/navrajnarula/Desktop/ffmpeg"
+
+
+script, filename = argv
+isSignificant = 0.8 #try different values.
+
+# P: list of probabilities
+Result, P, classNames = aT.fileClassification(filename, "svmModel", "svm")
+print("result is", Result)
+print("classNames is", classNames)
+print("P is", P)
+print("result is", Result)
+winner = np.argmax(P) #pick the result with the highest probability value.
+
+# is the highest value found above the isSignificant threshhold? 
+#if P[winner] > isSignificant :
+print("File: " +filename + " is in category: " + classNames[winner] + ", with probability: " + str(P[winner]))
+#else :
+ #print("Can't classify sound: " + str(P))