start of doing something

.gitignore

@@ -0,0 +1,2 @@
+.idea
+*.pyc

analyzeMovieSound.py

@@ -0,0 +1,195 @@
+import os, sys, shutil, glob, numpy, csv, cPickle
+import scipy.io.wavfile as wavfile
+import audioBasicIO
+import audioTrainTest as aT
+import audioSegmentation as aS
+import matplotlib.pyplot as plt
+import scipy.spatial.distance
+minDuration = 7;
+
+def classifyFolderWrapper(inputFolder, modelType, modelName, outputMode=False):
+	if not os.path.isfile(modelName):
+		raise Exception("Input modelName not found!")
+
+	if modelType=='svm':
+		[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadSVModel(modelName)
+	elif modelType=='knn':
+		[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadKNNModel(modelName)
+
+	PsAll = numpy.zeros((len(classNames), ))	
+
+	files = "*.wav"
+	if os.path.isdir(inputFolder):
+		strFilePattern = os.path.join(inputFolder, files)
+	else:
+		strFilePattern = inputFolder + files
+
+	wavFilesList = []
+	wavFilesList.extend(glob.glob(strFilePattern))
+	wavFilesList = sorted(wavFilesList)
+	if len(wavFilesList)==0:
+		print "No WAV files found!"
+		return 
+
+	Results = []
+	for wavFile in wavFilesList:	
+		[Fs, x] = audioBasicIO.readAudioFile(wavFile)	
+		signalLength = x.shape[0] / float(Fs)
+		[Result, P, classNames] = aT.fileClassification(wavFile, modelName, modelType)					
+		PsAll += (numpy.array(P) * signalLength)		
+		Result = int(Result)
+		Results.append(Result)
+		if outputMode:
+			print "{0:s}\t{1:s}".format(wavFile,classNames[Result])
+	Results = numpy.array(Results)
+
+	# print distribution of classes:
+	[Histogram, _] = numpy.histogram(Results, bins=numpy.arange(len(classNames)+1))
+	if outputMode:	
+		for i,h in enumerate(Histogram):
+			print "{0:20s}\t\t{1:d}".format(classNames[i], h)
+	PsAll = PsAll / numpy.sum(PsAll)
+
+
+	if outputMode:	
+		fig = plt.figure()
+		ax = fig.add_subplot(111)
+		plt.title("Classes percentage " + inputFolder.replace('Segments',''))
+		ax.axis((0, len(classNames)+1, 0, 1))
+		ax.set_xticks(numpy.array(range(len(classNames)+1)))
+		ax.set_xticklabels([" "] + classNames)
+		ax.bar(numpy.array(range(len(classNames)))+0.5, PsAll)
+		plt.show()
+	return classNames, PsAll
+
+def getMusicSegmentsFromFile(inputFile):	
+	modelType = "svm"
+	modelName = "data/svmMovies8classes"
+
+	dirOutput = inputFile[0:-4] + "_musicSegments"
+
+	if os.path.exists(dirOutput) and dirOutput!=".":
+		shutil.rmtree(dirOutput)	
+	os.makedirs(dirOutput)	
+
+	[Fs, x] = audioBasicIO.readAudioFile(inputFile)	
+
+	if modelType=='svm':
+		[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadSVModel(modelName)
+	elif modelType=='knn':
+		[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadKNNModel(modelName)
+
+	flagsInd, classNames, acc, CM = aS.mtFileClassification(inputFile, modelName, modelType, plotResults = False, gtFile = "")
+	segs, classes = aS.flags2segs(flagsInd, mtStep)
+
+	for i, s in enumerate(segs):
+		if (classNames[int(classes[i])] == "Music") and (s[1] - s[0] >= minDuration):
+			strOut = "{0:s}{1:.3f}-{2:.3f}.wav".format(dirOutput+os.sep, s[0], s[1])	
+			wavfile.write( strOut, Fs, x[int(Fs*s[0]):int(Fs*s[1])])
+
+def analyzeDir(dirPath):
+	for i,f in enumerate(glob.glob(dirPath + os.sep + '*.wav')):				# for each WAV file					
+		getMusicSegmentsFromFile(f)	
+		[c, P]= classifyFolderWrapper(f[0:-4] + "_musicSegments", "svm", "data/svmMusicGenre8", False)
+		if i==0:
+			print "".ljust(100)+"\t",
+			for C in c:
+				print C.ljust(12)+"\t",
+			print
+		print f.ljust(100)+"\t",
+		for p in P:
+				print "{0:.2f}".format(p).ljust(12)+"\t",
+		print
+
+def main(argv):	
+
+	if argv[1]=="--file":
+		getMusicSegmentsFromFile(argv[2])	
+		classifyFolderWrapper(argv[2][0:-4] + "_musicSegments", "svm", "data/svmMusicGenre8", True)		
+
+	elif argv[1]=="--dir":	
+		analyzeDir(argv[2])	
+
+	elif argv[1]=="--sim":
+		csvFile = argv[2]
+		f = []
+		fileNames = []
+		with open(csvFile, 'rb') as csvfile:
+			spamreader = csv.reader(csvfile, delimiter='\t', quotechar='|')
+			for j,row in enumerate(spamreader):
+				if j>0:
+					ftemp = []
+					for i in range(1,9):
+						ftemp.append(float(row[i]))
+					f.append(ftemp)
+					R = row[0]
+					II = R.find(".wav");
+					fileNames.append(row[0][0:II])
+			f = numpy.array(f)
+
+			Sim = numpy.zeros((f.shape[0], f.shape[0]))
+			for i in range(f.shape[0]):
+				for j in range(f.shape[0]):	
+					Sim[i,j] = scipy.spatial.distance.cdist(numpy.reshape(f[i,:], (f.shape[1],1)).T, numpy.reshape(f[j,:], (f.shape[1],1)).T, 'cosine')
+
+			Sim1 = numpy.reshape(Sim, (Sim.shape[0]*Sim.shape[1], 1))
+			plt.hist(Sim1)
+			plt.show()
+
+			fo = open(csvFile + "_simMatrix", "wb")
+			cPickle.dump(fileNames,  fo, protocol = cPickle.HIGHEST_PROTOCOL)
+			cPickle.dump(f, fo, protocol = cPickle.HIGHEST_PROTOCOL)			
+			cPickle.dump(Sim, fo, protocol = cPickle.HIGHEST_PROTOCOL)
+			fo.close()
+
+	elif argv[1]=="--loadsim":
+		try:
+			fo = open(argv[2], "rb")
+		except IOError:
+				print "didn't find file"
+				return
+		try:			
+			fileNames 	= cPickle.load(fo)
+			f 			= cPickle.load(fo)
+			Sim 		= cPickle.load(fo)
+		except:
+			fo.close()
+		fo.close()	
+		print fileNames
+		Sim1 = numpy.reshape(Sim, (Sim.shape[0]*Sim.shape[1], 1))
+		plt.hist(Sim1)
+		plt.show()
+
+	elif argv[1]=="--audio-event-dir":		
+		files = "*.wav"
+		inputFolder = argv[2]
+		if os.path.isdir(inputFolder):
+			strFilePattern = os.path.join(inputFolder, files)
+		else:
+			strFilePattern = inputFolder + files
+
+		wavFilesList = []
+		wavFilesList.extend(glob.glob(strFilePattern))
+		wavFilesList = sorted(wavFilesList)		
+		for i,w in enumerate(wavFilesList):			
+			[flagsInd, classesAll, acc, CM] = aS.mtFileClassification(w, "data/svmMovies8classes", "svm", False, '')
+			histTemp = numpy.zeros( (len(classesAll), ) )
+			for f in flagsInd:
+				histTemp[int(f)] += 1.0
+			histTemp /= histTemp.sum()
+
+			if i==0:
+				print "".ljust(100)+"\t",
+				for C in classesAll:
+					print C.ljust(12)+"\t",
+				print
+			print w.ljust(100)+"\t",
+			for h in histTemp:				
+				print "{0:.2f}".format(h).ljust(12)+"\t",
+			print
+
+
+	return 0
+
+if __name__ == '__main__':
+	main(sys.argv)

audacityAnnotation2WAVs.py

@@ -0,0 +1,67 @@
+import glob
+import os
+import audioBasicIO
+import sys
+import csv
+import scipy.io.wavfile as wavfile
+
+
+def annotation2files(wavFile, csvFile):
+    '''
+        Break an audio stream to segments of interest, 
+        defined by a csv file
+        
+        - wavFile:    path to input wavfile
+        - csvFile:    path to csvFile of segment limits
+        
+        Input CSV file must be of the format <T1>\t<T2>\t<Label>
+    '''    
+
+    [Fs, x] = audioBasicIO.readAudioFile(wavFile)
+    with open(csvFile, 'rb') as csvfile:
+        reader = csv.reader(csvfile, delimiter='\t', quotechar='|')
+        for j, row in enumerate(reader):
+            T1 = float(row[0].replace(",","."))
+            T2 = float(row[1].replace(",","."))            
+            label = "%s_%s_%.2f_%.2f.wav" % (wavFile, row[2], T1, T2)
+            label = label.replace(" ", "_")
+            xtemp = x[int(round(T1*Fs)):int(round(T2*Fs))]            
+            print T1, T2, label, xtemp.shape
+            wavfile.write(label, Fs, xtemp)  
+
+def main(argv):
+    if argv[1] == "-f":
+        wavFile = argv[2]
+        annotationFile = argv[3]
+        annotation2files(wavFile, annotationFile)
+    elif argv[1] == "-d":
+        inputFolder = argv[2]
+        types = ('*.txt', '*.csv')
+        annotationFilesList = []
+        for files in types:
+            annotationFilesList.extend(glob.glob(os.path.join(inputFolder, files)))
+        for anFile in annotationFilesList:
+            wavFile = os.path.splitext(anFile)[0] + ".wav"
+            if not os.path.isfile(wavFile):
+                wavFile = os.path.splitext(anFile)[0] + ".mp3"
+                if not os.path.isfile(wavFile):
+                    print "Audio file not found!"
+                    return
+            annotation2files(wavFile, anFile)
+
+
+if __name__ == '__main__':
+    # Used to extract a series of annotated WAV files based on (a) an audio file (mp3 or wav) and 
+    # (b) a segment annotation file e.g. a "label" file generated in audacity
+    #
+    # usage 1:
+    # python audacityAnnotation2WAVs.py -f <audiofilepath> <annotationfilepath>
+    # The <annotationfilepath> is actually a tab-seperated file where each line has the format <startTime>\t<entTime>\t<classLabel>
+    # The result of this process is a  series of WAV files with a file name <audiofilepath>_<startTime>_<endTime>_<classLabel>
+    # 
+    # usage 2:
+    # python audacityAnnotation2WAVs.py -d <annotationfolderpath>
+    # Same but searches all .txt and .csv annotation files. Audio files are supposed to be in the same path / filename with a WAV extension
+
+    main(sys.argv)
+