Пример #1
0
def regressionFileWrapper(inputFile, modelType, modelName):
    if not os.path.isfile(inputFile):
        raise Exception("Input audio file not found!")

    R, regressionNames = aT.fileRegression(inputFile, modelName, modelType)
    for i in range(len(R)):
        print "{0:s}\t{1:.3f}".format(regressionNames[i], R[i])
Пример #2
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def regressionFolderWrapper(inputFolder, modelType, modelName):
    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:
        R, regressionNames = aT.fileRegression(wavFile, modelName, modelType)
        Results.append(R)
    Results = numpy.array(Results)

    for i, r in enumerate(regressionNames):
        [Histogram, bins] = numpy.histogram(Results[:, i])
        centers = (bins[0:-1] + bins[1::]) / 2.0
        plt.subplot(len(regressionNames), 1, i)
        plt.plot(centers, Histogram)
        plt.title(r)
    plt.show()
Пример #3
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def regressionFolderWrapper(inputFolder, modelType, modelName):
    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:
        R, regressionNames = aT.fileRegression(wavFile, modelName, modelType)
        Results.append(R)
    Results = numpy.array(Results)

    for i, r in enumerate(regressionNames):
        [Histogram, bins] = numpy.histogram(Results[:, i])
        centers = (bins[0:-1] + bins[1::]) / 2.0
        plt.subplot(len(regressionNames), 1, i)
        plt.plot(centers, Histogram)
        plt.title(r)
    plt.show()
Пример #4
0
def regressionFileWrapper(inputFile, modelType, modelName):
    if not os.path.isfile(inputFile):
        raise Exception("Input audio file not found!")

    R, regressionNames = aT.fileRegression(inputFile, modelName, modelType)
    for i in range(len(R)):
        print "{0:s}\t{1:.3f}".format(regressionNames[i], R[i])
Пример #5
0
def detection():
    if request.method == 'POST':
        f = dict(request.files)['file'][0]
        filename = secure_filename(f.filename)
        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        f.save(filepath)

        if (filepath.endswith(".wav")):
            result = aT.fileRegression(filepath, "training/", "svm")
            os.remove(filepath)
        else:
            wavfilepath = os.path.splitext(filepath)[0] + '.wav'
            subprocess.call(['ffmpeg', '-i', filepath, wavfilepath])
            result = aT.fileRegression(wavfilepath, "training/", "svm")
            os.remove(wavfilepath)

        print(result)
        return jsonify(dict(zip(result[1], result[0])))
Пример #6
0
def main(argv):
    if argv[1] == "-dirMp3toWAV":  # convert mp3 to wav (batch)
        if len(argv) == 5:
            path = argv[2]
            if argv[3] not in ["8000", "16000", "32000", "44100"]:
                print "Error. Unsupported sampling rate (must be: 8000, 16000, 32000 or 44100)."
                return
            if argv[4] not in ["1", "2"]:
                print "Error. Number of output channels must be 1 or 2"
                return
            if not os.path.isdir(path):
                raise Exception("Input path not found!")
            useMp3TagsAsNames = True
            audioBasicIO.convertDirMP3ToWav(path, int(argv[3]), int(argv[4]),
                                            useMp3TagsAsNames)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -dirMp3toWAV <dirName> <sampling Freq> <numOfChannels>"

    if argv[1] == "-dirWAVChangeFs":  # convert mp3 to wav (batch)
        if len(argv) == 5:
            path = argv[2]
            if argv[3] not in ["8000", "16000", "32000", "44100"]:
                print "Error. Unsupported sampling rate (must be: 8000, 16000, 32000 or 44100)."
                return
            if argv[4] not in ["1", "2"]:
                print "Error. Number of output channels must be 1 or 2"
                return
            if not os.path.isdir(path):
                raise Exception("Input path not found!")
            audioBasicIO.convertFsDirWavToWav(path, int(argv[3]), int(argv[4]))
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -dirMp3toWAV <dirName> <sampling Freq> <numOfChannels>"

    elif argv[
            1] == "-featureExtractionFile":  # short-term and mid-term feature extraction to files (csv and numpy)
        if len(argv) == 7:
            wavFileName = argv[2]
            if not os.path.isfile(wavFileName):
                raise Exception("Input audio file not found!")
            if not (uT.isNum(argv[3]) and uT.isNum(argv[4])
                    and uT.isNum(argv[5]) and uT.isNum(argv[6])):
                raise Exception(
                    "Mid-term and short-term window sizes and steps must be numbers!"
                )
            mtWin = float(argv[3])
            mtStep = float(argv[4])
            stWin = float(argv[5])
            stStep = float(argv[6])
            outFile = wavFileName
            aF.mtFeatureExtractionToFile(wavFileName, mtWin, mtStep, stWin,
                                         stStep, outFile, True, True, True)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -featureExtractionFile <wavFileName> <mtWin> <mtStep> <stWin> <stStep>"

    elif argv[1] == "-beatExtraction":
        if len(argv) == 4:
            wavFileName = argv[2]
            if not os.path.isfile(wavFileName):
                raise Exception("Input audio file not found!")
            if not (uT.isNum(argv[3])):
                raise Exception("PLOT must be either 0 or 1")
            if not ((int(argv[3]) == 0) or (int(argv[3]) == 1)):
                raise Exception("PLOT must be either 0 or 1")

            [Fs, x] = audioBasicIO.readAudioFile(wavFileName)
            F = aF.stFeatureExtraction(x, Fs, 0.050 * Fs, 0.050 * Fs)
            BPM, ratio = aF.beatExtraction(F, 0.050, int(argv[3]) == 1)
            print "Beat: {0:d} bpm ".format(int(BPM))
            print "Ratio: {0:.2f} ".format(ratio)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -beatExtraction <wavFileName> <PLOT (0 or 1)>"

    elif argv[
            1] == '-featureExtractionDir':  # same as -featureExtractionFile, in a batch mode (i.e. for each WAV file in the provided path)
        if len(argv) == 7:
            path = argv[2]
            if not os.path.isdir(path):
                raise Exception("Input path not found!")
            if not (uT.isNum(argv[3]) and uT.isNum(argv[4])
                    and uT.isNum(argv[5]) and uT.isNum(argv[6])):
                raise Exception(
                    "Mid-term and short-term window sizes and steps must be numbers!"
                )
            mtWin = float(argv[3])
            mtStep = float(argv[4])
            stWin = float(argv[5])
            stStep = float(argv[6])
            aF.mtFeatureExtractionToFileDir(path, mtWin, mtStep, stWin, stStep,
                                            True, True, True)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -featureExtractionDir <path> <mtWin> <mtStep> <stWin> <stStep>"

    elif argv[
            1] == '-featureVisualizationDir':  # visualize the content relationships between recordings stored in a folder
        if len(argv) == 3:
            if not os.path.isdir(argv[2]):
                raise Exception("Input folder not found!")
            aV.visualizeFeaturesFolder(argv[2], "pca", "")

    elif argv[
            1] == '-fileSpectrogram':  # show spectogram of a sound stored in a file
        if len(argv) == 3:
            wavFileName = argv[2]
            if not os.path.isfile(wavFileName):
                raise Exception("Input audio file not found!")
            [Fs, x] = audioBasicIO.readAudioFile(wavFileName)
            x = audioBasicIO.stereo2mono(x)
            specgram, TimeAxis, FreqAxis = aF.stSpectogram(
                x, Fs, round(Fs * 0.040), round(Fs * 0.040), True)
        else:
            print "Error.\nSyntax: " + argv[0] + " -fileSpectrogram <fileName>"

    elif argv[
            1] == '-fileChromagram':  # show spectogram of a sound stored in a file
        if len(argv) == 3:
            wavFileName = argv[2]
            if not os.path.isfile(wavFileName):
                raise Exception("Input audio file not found!")
            [Fs, x] = audioBasicIO.readAudioFile(wavFileName)
            x = audioBasicIO.stereo2mono(x)
            specgram, TimeAxis, FreqAxis = aF.stChromagram(
                x, Fs, round(Fs * 0.040), round(Fs * 0.040), True)
        else:
            print "Error.\nSyntax: " + argv[0] + " -fileSpectrogram <fileName>"

    elif argv[1] == "-trainClassifier":  # Segment classifier training (OK)
        if len(argv) > 6:
            method = argv[2]
            beatFeatures = (int(argv[3]) == 1)
            listOfDirs = argv[4:len(argv) - 1]
            modelName = argv[-1]
            aT.featureAndTrain(listOfDirs,
                               1,
                               1,
                               aT.shortTermWindow,
                               aT.shortTermStep,
                               method.lower(),
                               modelName,
                               computeBEAT=beatFeatures)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -trainClassifier <method(svm or knn)> <beat features> <directory 1> <directory 2> ... <directory N> <modelName>"

    elif argv[1] == "-trainRegression":  # Segment regression model
        if len(argv) == 6:
            method = argv[2]
            beatFeatures = (int(argv[3]) == 1)
            dirName = argv[4]
            modelName = argv[5]
            aT.featureAndTrainRegression(dirName,
                                         1,
                                         1,
                                         aT.shortTermWindow,
                                         aT.shortTermStep,
                                         method.lower(),
                                         modelName,
                                         computeBEAT=beatFeatures)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -trainRegression <method(svm or knn)> <beat features> <directory> <modelName>"

    elif argv[1] == "-classifyFile":  # Single File Classification (OK)
        if len(argv) == 5:
            modelType = argv[2]
            modelName = argv[3]
            inputFile = argv[4]

            if modelType not in ["svm", "knn"]:
                raise Exception("ModelType has to be either svm or knn!")
            if not os.path.isfile(modelName):
                raise Exception("Input modelName not found!")
            if not os.path.isfile(inputFile):
                raise Exception("Input audio file not found!")

            [Result, P,
             classNames] = aT.fileClassification(inputFile, modelName,
                                                 modelType)
            print "{0:s}\t{1:s}".format("Class", "Probability")
            for i, c in enumerate(classNames):
                print "{0:s}\t{1:.2f}".format(c, P[i])
            print "Winner class: " + classNames[int(Result)]
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -classifyFile <method(svm or knn)> <modelName> <fileName>"

    elif argv[1] == "-regressionFile":  # Single File Classification (OK)
        if len(argv) == 5:
            modelType = argv[2]
            modelName = argv[3]
            inputFile = argv[4]

            if modelType not in ["svm", "knn"]:
                raise Exception("ModelType has to be either svm or knn!")
            if not os.path.isfile(inputFile):
                raise Exception("Input audio file not found!")

            R, regressionNames = aT.fileRegression(inputFile, modelName,
                                                   modelType)
            for i in range(len(R)):
                print "{0:s}\t{1:.3f}".format(regressionNames[i], R[i])

            #print "{0:s}\t{1:.2f}".format(c,P[i])

        else:
            print "Error.\nSyntax: " + argv[
                0] + " -regressionFile <method(svm or knn)> <modelName> <fileName>"

    elif argv[1] == "-classifyFolder":  # Directory classification (Ok)
        if len(argv) == 6 or len(argv) == 5:
            modelType = argv[2]
            modelName = argv[3]
            inputFolder = argv[4]
            if len(argv) == 6:
                outputMode = argv[5]
            else:
                outputMode = "0"

            if modelType not in ["svm", "knn"]:
                raise Exception("ModelType has to be either svm or knn!")
            if outputMode not in ["0", "1"]:
                raise Exception("outputMode has to be 0 or 1")
            if not os.path.isfile(modelName):
                raise Exception("Input modelName not found!")
            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:
                [Result, P,
                 classNames] = aT.fileClassification(wavFile, modelName,
                                                     modelType)
                Result = int(Result)
                Results.append(Result)
                if outputMode == "1":
                    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))
            for i, h in enumerate(Histogram):
                print "{0:20s}\t\t{1:d}".format(classNames[i], h)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -classifyFolder <method(svm or knn)> <modelName> <folderName> <outputMode(0 or 1)"

    elif argv[
            1] == "-regressionFolder":  # Regression applied on the WAV files of a folder
        if len(argv) == 5:
            modelType = argv[2]
            modelName = argv[3]
            inputFolder = argv[4]

            if modelType not in ["svm", "knn"]:
                raise Exception("ModelType has to be either svm or knn!")

            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:
                R, regressionNames = aT.fileRegression(wavFile, modelName,
                                                       modelType)
                Results.append(R)
            Results = numpy.array(Results)
            for i, r in enumerate(regressionNames):
                [Histogram, bins] = numpy.histogram(Results[:, i])
                centers = (bins[0:-1] + bins[1::]) / 2.0
                plt.subplot(len(regressionNames), 1, i)
                plt.plot(centers, Histogram)
                plt.title(r)
            plt.show()


#					for h in Histogram:
#						print "{0:20d}".format(h),
#				if outputMode=="1":
#					for i,h in enumerate(Histogram):
#						print "{0:20s}\t\t{1:d}".format(classNames[i], h)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -regressionFolder <method(svm or knn)> <modelName> <folderName>"

    elif argv[1] == '-trainHMMsegmenter_fromfile':
        if len(argv) == 7:
            wavFile = argv[2]
            gtFile = argv[3]
            hmmModelName = argv[4]
            if not uT.isNum(argv[5]):
                print "Error: mid-term window size must be float!"
                return
            if not uT.isNum(argv[6]):
                print "Error: mid-term window step must be float!"
                return
            mtWin = float(argv[5])
            mtStep = float(argv[6])
            if not os.path.isfile(wavFile):
                print "Error: wavfile does not exist!"
                return
            if not os.path.isfile(gtFile):
                print "Error: groundtruth does not exist!"
                return
            aS.trainHMM_fromFile(wavFile, gtFile, hmmModelName, mtWin, mtStep)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -trainHMMsegmenter_fromfile <wavFilePath> <gtSegmentFilePath> <hmmModelFileName> <mtWin> <mtStep>"

    elif argv[1] == '-trainHMMsegmenter_fromdir':
        if len(argv) == 6:
            dirPath = argv[2]
            hmmModelName = argv[3]
            if not uT.isNum(argv[4]):
                print "Error: mid-term window size must be float!"
            if not uT.isNum(argv[5]):
                print "Error: mid-term window step must be float!"
            mtWin = float(argv[4])
            mtStep = float(argv[5])
            aS.trainHMM_fromDir(dirPath, hmmModelName, mtWin, mtStep)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -trainHMMsegmenter_fromdir <dirPath> <hmmModelFileName> <mtWin> <mtStep>"

    elif argv[
            1] == "-segmentClassifyFileHMM":  # HMM-based segmentation-classification
        if len(argv) == 4:
            hmmModelName = argv[2]
            wavFile = argv[3]
            gtFile = wavFile.replace('.wav', '.segments')
            aS.hmmSegmentation(wavFile,
                               hmmModelName,
                               PLOT=True,
                               gtFileName=gtFile)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -segmentClassifyHMM <hmmModelName> <fileName>"

    elif argv[
            1] == '-segmentClassifyFile':  # Segmentation-classification (fix-sized segment using knn or svm)
        if (len(argv) == 5):
            modelType = argv[2]
            modelName = argv[3]
            inputWavFile = argv[4]

            if modelType not in ["svm", "knn"]:
                raise Exception("ModelType has to be either svm or knn!")
            if not os.path.isfile(modelName):
                raise Exception("Input modelName not found!")
            if not os.path.isfile(inputWavFile):
                raise Exception("Input audio file not found!")
            gtFile = inputWavFile.replace('.wav', '.segments')
            aS.mtFileClassification(inputWavFile, modelName, modelType, True,
                                    gtFile)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -segmentClassifyFile <method(svm or knn)> <modelName> <fileName>"

    elif argv[1] == "-segmentationEvaluation":
        if len(argv) == 5:
            methodName = argv[2]
            modelName = argv[3]
            dirName = argv[4]
            aS.evaluateSegmentationClassificationDir(dirName, modelName,
                                                     methodName)
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -segmentationEvaluation <method(svm or knn)> <modelName> <directoryName>"

    elif argv[1] == "-silenceRemoval":
        if len(argv) == 5:
            inputFile = argv[2]
            if not os.path.isfile(inputFile):
                raise Exception("Input audio file not found!")

            smoothingWindow = float(argv[3])
            weight = float(argv[4])
            [Fs,
             x] = audioBasicIO.readAudioFile(inputFile)  # read audio signal
            segmentLimits = aS.silenceRemoval(x, Fs, 0.05, 0.05,
                                              smoothingWindow, weight,
                                              False)  # get onsets
            for i, s in enumerate(segmentLimits):
                strOut = "{0:s}_{1:.3f}-{2:.3f}.wav".format(
                    inputFile[0:-4], s[0], s[1])
                wavfile.write(strOut, Fs, x[int(Fs * s[0]):int(Fs * s[1])])
        else:
            print "Error.\nSyntax: " + argv[
                0] + " -silenceRemoval <inputFile> <smoothinWindow(secs)> <Threshold Weight>"

    elif argv[
            1] == '-speakerDiarization':  # speaker diarization (from file): TODO
        inputFile = argv[2]
        nSpeakers = int(argv[3])
        useLDA = (int(argv[4]) == 1)
        if useLDA:
            aS.speakerDiarization(inputFile, nSpeakers, PLOT=True)
        else:
            aS.speakerDiarization(inputFile, nSpeakers, LDAdim=0, PLOT=True)
        #print speechLimits

    elif argv[1] == "-speakerDiarizationScriptEval":
        dir = argv[2]
        listOfLDAs = [int(l) for l in argv[3::]]
        aS.speakerDiarizationEvaluateScript(dir, listOfLDAs)

    elif argv[1] == '-thumbnail':  # music thumbnailing (OK)
        if len(argv) == 4:
            inputFile = argv[2]
            stWindow = 1.0
            stStep = 1.0
            if not os.path.isfile(inputFile):
                raise Exception("Input audio file not found!")

            [Fs, x] = audioBasicIO.readAudioFile(inputFile)  # read file
            if Fs == -1:  # could not read file
                return
            try:
                thumbnailSize = float(argv[3])
            except ValueError:
                print "Thumbnail size must be a float (in seconds)"
                return
            [A1, A2, B1, B2, Smatrix] = aS.musicThumbnailing(
                x, Fs, stWindow, stStep,
                thumbnailSize)  # find thumbnail endpoints

            # write thumbnails to WAV files:
            thumbnailFileName1 = inputFile.replace(".wav", "_thumb1.wav")
            thumbnailFileName2 = inputFile.replace(".wav", "_thumb2.wav")
            wavfile.write(thumbnailFileName1, Fs, x[int(Fs * A1):int(Fs * A2)])
            wavfile.write(thumbnailFileName2, Fs, x[int(Fs * B1):int(Fs * B2)])
            print "1st thumbnail (stored in file {0:s}): {1:4.1f}sec -- {2:4.1f}sec".format(
                thumbnailFileName1, A1, A2)
            print "2nd thumbnail (stored in file {0:s}): {1:4.1f}sec -- {2:4.1f}sec".format(
                thumbnailFileName2, B1, B2)

            # Plot self-similarity matrix:
            fig = plt.figure()
            ax = fig.add_subplot(111, aspect='auto')
            plt.imshow(Smatrix)
            # Plot best-similarity diagonal:
            Xcenter = (A1 / stStep + A2 / stStep) / 2.0
            Ycenter = (B1 / stStep + B2 / stStep) / 2.0

            e1 = matplotlib.patches.Ellipse((Ycenter, Xcenter),
                                            thumbnailSize * 1.4,
                                            3,
                                            angle=45,
                                            linewidth=3,
                                            fill=False)
            ax.add_patch(e1)

            plt.plot([B1, Smatrix.shape[0]], [A1, A1],
                     color='k',
                     linestyle='--',
                     linewidth=2)
            plt.plot([B2, Smatrix.shape[0]], [A2, A2],
                     color='k',
                     linestyle='--',
                     linewidth=2)
            plt.plot([B1, B1], [A1, Smatrix.shape[0]],
                     color='k',
                     linestyle='--',
                     linewidth=2)
            plt.plot([B2, B2], [A2, Smatrix.shape[0]],
                     color='k',
                     linestyle='--',
                     linewidth=2)

            plt.xlim([0, Smatrix.shape[0]])
            plt.ylim([Smatrix.shape[1], 0])

            ax.yaxis.set_label_position("right")
            ax.yaxis.tick_right()

            plt.xlabel('frame no')
            plt.ylabel('frame no')
            plt.title('Self-similarity matrix')

            plt.show()

        else:
            print "Error.\nSyntax: " + argv[
                0] + " -thumbnail <filename> <thumbnailsize(seconds)>"
Пример #7
0
def main(argv):
	if argv[1] == "-dirMp3toWAV":				# convert mp3 to wav (batch)
		if len(argv)==5:			
			path = argv[2]
			if argv[3] not in ["8000", "16000", "32000", "44100"]:
				print "Error. Unsupported sampling rate (must be: 8000, 16000, 32000 or 44100)."; return
			if argv[4] not in ["1","2"]:
				print "Error. Number of output channels must be 1 or 2"; return
			if not os.path.isdir(path):
				raise Exception("Input path not found!")
			useMp3TagsAsNames = True
			audioBasicIO.convertDirMP3ToWav(path, int(argv[3]), int(argv[4]), useMp3TagsAsNames)
		else:
			print "Error.\nSyntax: " + argv[0] + " -dirMp3toWAV <dirName> <sampling Freq> <numOfChannels>"

	if argv[1] == "-dirWAVChangeFs":				# convert mp3 to wav (batch)
		if len(argv)==5:			
			path = argv[2]
			if argv[3] not in ["8000", "16000", "32000", "44100"]:
				print "Error. Unsupported sampling rate (must be: 8000, 16000, 32000 or 44100)."; return
			if argv[4] not in ["1","2"]:
				print "Error. Number of output channels must be 1 or 2"; return
			if not os.path.isdir(path):
				raise Exception("Input path not found!")
			audioBasicIO.convertFsDirWavToWav(path, int(argv[3]), int(argv[4]))
		else:
			print "Error.\nSyntax: " + argv[0] + " -dirMp3toWAV <dirName> <sampling Freq> <numOfChannels>"

	elif argv[1] == "-featureExtractionFile":		# short-term and mid-term feature extraction to files (csv and numpy)
		if len(argv)==7:
			wavFileName = argv[2]
			if not os.path.isfile(wavFileName):
				raise Exception("Input audio file not found!")
			if not (uT.isNum(argv[3]) and uT.isNum(argv[4]) and uT.isNum(argv[5]) and uT.isNum(argv[6])):
				raise Exception("Mid-term and short-term window sizes and steps must be numbers!")
			mtWin = float(argv[3])
			mtStep = float(argv[4])
			stWin = float(argv[5])
			stStep = float(argv[6])
			outFile = wavFileName
			aF.mtFeatureExtractionToFile(wavFileName, mtWin, mtStep, stWin, stStep, outFile, True, True, True)
		else:
			print "Error.\nSyntax: " + argv[0] + " -featureExtractionFile <wavFileName> <mtWin> <mtStep> <stWin> <stStep>"

	elif argv[1] == "-beatExtraction":
		if len(argv)==4:
			wavFileName = argv[2]
			if not os.path.isfile(wavFileName):
				raise Exception("Input audio file not found!")
			if not (uT.isNum(argv[3])):
				raise Exception("PLOT must be either 0 or 1")
			if not ( (int(argv[3]) == 0) or (int(argv[3]) == 1) ):
				raise Exception("PLOT must be either 0 or 1")

			[Fs, x] = audioBasicIO.readAudioFile(wavFileName);
			F = aF.stFeatureExtraction(x, Fs, 0.050*Fs, 0.050*Fs);
			BPM, ratio = aF.beatExtraction(F, 0.050, int(argv[3])==1)
			print "Beat: {0:d} bpm ".format(int(BPM))
			print "Ratio: {0:.2f} ".format(ratio)
		else:
			print "Error.\nSyntax: " + argv[0] + " -beatExtraction <wavFileName> <PLOT (0 or 1)>"


	elif argv[1] == '-featureExtractionDir':	# same as -featureExtractionFile, in a batch mode (i.e. for each WAV file in the provided path)
		if len(argv)==7:
			path = argv[2]
			if not os.path.isdir(path):
				raise Exception("Input path not found!")
			if not (uT.isNum(argv[3]) and uT.isNum(argv[4]) and uT.isNum(argv[5]) and uT.isNum(argv[6])):
				raise Exception("Mid-term and short-term window sizes and steps must be numbers!")
			mtWin = float(argv[3])
			mtStep = float(argv[4])
			stWin = float(argv[5])
			stStep = float(argv[6])
			aF.mtFeatureExtractionToFileDir(path, mtWin, mtStep, stWin, stStep, True, True, True)
		else:
			print "Error.\nSyntax: " + argv[0] + " -featureExtractionDir <path> <mtWin> <mtStep> <stWin> <stStep>"

	elif argv[1] == '-featureVisualizationDir':	# visualize the content relationships between recordings stored in a folder
		if len(argv)==3:
			if not os.path.isdir(argv[2]):
				raise Exception("Input folder not found!")
			aV.visualizeFeaturesFolder(argv[2], "pca", "")

	elif argv[1] == '-fileSpectrogram':		# show spectogram of a sound stored in a file
			if len(argv)==3:
				wavFileName = argv[2]		
				if not os.path.isfile(wavFileName):
					raise Exception("Input audio file not found!")
				[Fs, x] = audioBasicIO.readAudioFile(wavFileName)
				x = audioBasicIO.stereo2mono(x)
				specgram, TimeAxis, FreqAxis = aF.stSpectogram(x, Fs, round(Fs*0.040), round(Fs*0.040), True)
			else:
				print "Error.\nSyntax: " + argv[0] + " -fileSpectrogram <fileName>"

	elif argv[1] == '-fileChromagram':		# show spectogram of a sound stored in a file
			if len(argv)==3:
				wavFileName = argv[2]		
				if not os.path.isfile(wavFileName):
					raise Exception("Input audio file not found!")
				[Fs, x] = audioBasicIO.readAudioFile(wavFileName)
				x = audioBasicIO.stereo2mono(x)
				specgram, TimeAxis, FreqAxis = aF.stChromagram(x, Fs, round(Fs*0.040), round(Fs*0.040), True)
			else:
				print "Error.\nSyntax: " + argv[0] + " -fileSpectrogram <fileName>"


	elif argv[1] == "-trainClassifier": 		# Segment classifier training (OK)
			if len(argv)>6: 
				method = argv[2]
				beatFeatures = (int(argv[3])==1)
				listOfDirs = argv[4:len(argv)-1]
				modelName = argv[-1]			
				aT.featureAndTrain(listOfDirs, 1, 1, aT.shortTermWindow, aT.shortTermStep, method.lower(), modelName, computeBEAT = beatFeatures)
			else:
				print "Error.\nSyntax: " + argv[0] + " -trainClassifier <method(svm or knn)> <beat features> <directory 1> <directory 2> ... <directory N> <modelName>"

	elif argv[1] == "-trainRegression": 		# Segment regression model
			if len(argv)==6: 
				method = argv[2]
				beatFeatures = (int(argv[3])==1)
				dirName = argv[4]
				modelName = argv[5]			
				aT.featureAndTrainRegression(dirName, 1, 1, aT.shortTermWindow, aT.shortTermStep, method.lower(), modelName, computeBEAT = beatFeatures)
			else:
				print "Error.\nSyntax: " + argv[0] + " -trainRegression <method(svm or knn)> <beat features> <directory> <modelName>"

	elif argv[1] == "-classifyFile":		# Single File Classification (OK)
			if len(argv)==5: 
				modelType = argv[2]
				modelName = argv[3]
				inputFile = argv[4]

				if modelType not in ["svm", "knn"]:
					raise Exception("ModelType has to be either svm or knn!")
				if not os.path.isfile(modelName):
					raise Exception("Input modelName not found!")
				if not os.path.isfile(inputFile):
					raise Exception("Input audio file not found!")

				[Result, P, classNames] = aT.fileClassification(inputFile, modelName, modelType)
				print "{0:s}\t{1:s}".format("Class","Probability")
				for i,c in enumerate(classNames):
					print "{0:s}\t{1:.2f}".format(c,P[i])
				print "Winner class: " + classNames[int(Result)]
			else:
				print "Error.\nSyntax: " + argv[0] + " -classifyFile <method(svm or knn)> <modelName> <fileName>"

	elif argv[1] == "-regressionFile":		# Single File Classification (OK)
			if len(argv)==5: 
				modelType = argv[2]
				modelName = argv[3]
				inputFile = argv[4]

				if modelType not in ["svm", "knn"]:
					raise Exception("ModelType has to be either svm or knn!")
				if not os.path.isfile(inputFile):
					raise Exception("Input audio file not found!")

				R, regressionNames = aT.fileRegression(inputFile, modelName, modelType)
				for i in range(len(R)):
					print "{0:s}\t{1:.3f}".format(regressionNames[i], R[i])
				
				#print "{0:s}\t{1:.2f}".format(c,P[i])

			else:
				print "Error.\nSyntax: " + argv[0] + " -regressionFile <method(svm or knn)> <modelName> <fileName>"

	elif argv[1] == "-classifyFolder": 			# Directory classification (Ok)
			if len(argv)==6 or len(argv)==5: 
				modelType = argv[2]
				modelName = argv[3]
				inputFolder = argv[4]
				if len(argv)==6:
					outputMode = argv[5]
				else:
					outputMode = "0"

				if modelType not in ["svm", "knn"]:
					raise Exception("ModelType has to be either svm or knn!")
				if outputMode not in ["0","1"]:
					raise Exception("outputMode has to be 0 or 1")
				if not os.path.isfile(modelName):
					raise Exception("Input modelName not found!")
				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:	
					[Result, P, classNames] = aT.fileClassification(wavFile, modelName, modelType)	
					Result = int(Result)
					Results.append(Result)
					if outputMode=="1":
						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))
				for i,h in enumerate(Histogram):
					print "{0:20s}\t\t{1:d}".format(classNames[i], h)
			else:
				print "Error.\nSyntax: " + argv[0] + " -classifyFolder <method(svm or knn)> <modelName> <folderName> <outputMode(0 or 1)"

	elif argv[1] == "-regressionFolder": 			# Regression applied on the WAV files of a folder
			if len(argv)==5: 
				modelType = argv[2]
				modelName = argv[3]
				inputFolder = argv[4]

				if modelType not in ["svm", "knn"]:
					raise Exception("ModelType has to be either svm or knn!")

				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:	
					R, regressionNames = aT.fileRegression(wavFile, modelName, modelType)
					Results.append(R)
				Results = numpy.array(Results)
				for i, r in enumerate(regressionNames):
					[Histogram, bins] = numpy.histogram(Results[:, i])
					centers = (bins[0:-1] + bins[1::]) / 2.0
					plt.subplot(len(regressionNames), 1, i);
					plt.plot(centers, Histogram)
					plt.title(r)
				plt.show()
#					for h in Histogram:
#						print "{0:20d}".format(h),
#				if outputMode=="1":
#					for i,h in enumerate(Histogram):
#						print "{0:20s}\t\t{1:d}".format(classNames[i], h)
			else:
				print "Error.\nSyntax: " + argv[0] + " -regressionFolder <method(svm or knn)> <modelName> <folderName>"

	elif argv[1] == '-trainHMMsegmenter_fromfile':
		if len(argv)==7:
			wavFile = argv[2]
			gtFile = argv[3]
			hmmModelName = argv[4]
			if not uT.isNum(argv[5]):
				print "Error: mid-term window size must be float!"; return
			if not uT.isNum(argv[6]):
				print "Error: mid-term window step must be float!"; return
			mtWin = float(argv[5])
			mtStep = float(argv[6])
			if not os.path.isfile(wavFile):
				print "Error: wavfile does not exist!"; return
			if not os.path.isfile(gtFile):
				print "Error: groundtruth does not exist!"; return
			aS.trainHMM_fromFile(wavFile, gtFile, hmmModelName, mtWin, mtStep)
		else:
			print "Error.\nSyntax: " + argv[0] + " -trainHMMsegmenter_fromfile <wavFilePath> <gtSegmentFilePath> <hmmModelFileName> <mtWin> <mtStep>"

	elif argv[1] == '-trainHMMsegmenter_fromdir':
		if len(argv)==6:
			dirPath = argv[2]
			hmmModelName = argv[3]
			if not uT.isNum(argv[4]):
				print "Error: mid-term window size must be float!"
			if not uT.isNum(argv[5]):
				print "Error: mid-term window step must be float!"
			mtWin = float(argv[4])
			mtStep = float(argv[5])
			aS.trainHMM_fromDir(dirPath, hmmModelName, mtWin, mtStep)
		else:
			print "Error.\nSyntax: " + argv[0] + " -trainHMMsegmenter_fromdir <dirPath> <hmmModelFileName> <mtWin> <mtStep>"

	elif argv[1] == "-segmentClassifyFileHMM":	# HMM-based segmentation-classification
		if len(argv)==4:
			hmmModelName = argv[2]
			wavFile = argv[3]
			gtFile = wavFile.replace('.wav', '.segments');			
			aS.hmmSegmentation(wavFile, hmmModelName, PLOT = True, gtFileName = gtFile)
		else:
			print "Error.\nSyntax: " + argv[0] + " -segmentClassifyHMM <hmmModelName> <fileName>"

	elif argv[1] == '-segmentClassifyFile':		# Segmentation-classification (fix-sized segment using knn or svm)
		if (len(argv)==5):
			modelType = argv[2]
			modelName = argv[3]
			inputWavFile = argv[4]

			if modelType not in ["svm", "knn"]:
				raise Exception("ModelType has to be either svm or knn!")
			if not os.path.isfile(modelName):
				raise Exception("Input modelName not found!")
			if not os.path.isfile(inputWavFile):
				raise Exception("Input audio file not found!")
			gtFile = inputWavFile.replace('.wav', '.segments');
			aS.mtFileClassification(inputWavFile, modelName, modelType, True, gtFile)
		else:
			print "Error.\nSyntax: " + argv[0] + " -segmentClassifyFile <method(svm or knn)> <modelName> <fileName>"

	elif argv[1] == "-segmentationEvaluation":
		if len(argv)==5:
			methodName = argv[2]
			modelName = argv[3]
			dirName = argv[4]
			aS.evaluateSegmentationClassificationDir(dirName, modelName, methodName)
		else:
			print "Error.\nSyntax: " + argv[0] + " -segmentationEvaluation <method(svm or knn)> <modelName> <directoryName>"

	elif argv[1] == "-silenceRemoval":
		if len(argv)==5:
			inputFile = argv[2]
			if not os.path.isfile(inputFile):
				raise Exception("Input audio file not found!")

			smoothingWindow = float(argv[3])
			weight = float(argv[4])
			[Fs, x] = audioBasicIO.readAudioFile(inputFile)						# read audio signal
			segmentLimits = aS.silenceRemoval(x, Fs, 0.05, 0.05, smoothingWindow, weight, False)	# get onsets
			for i, s in enumerate(segmentLimits):
				strOut = "{0:s}_{1:.3f}-{2:.3f}.wav".format(inputFile[0:-4], s[0], s[1])
				wavfile.write( strOut, Fs, x[int(Fs*s[0]):int(Fs*s[1])])
		else:
			print "Error.\nSyntax: " + argv[0] + " -silenceRemoval <inputFile> <smoothinWindow(secs)> <Threshold Weight>"

	elif argv[1] == '-speakerDiarization':		# speaker diarization (from file): TODO				
			inputFile = argv[2]
			nSpeakers = int(argv[3])
			useLDA = (int(argv[4])==1)			
			if useLDA:
				aS.speakerDiarization(inputFile, nSpeakers, PLOT = True);
			else:
				aS.speakerDiarization(inputFile, nSpeakers, LDAdim = 0, PLOT = True);
			#print speechLimits

	elif argv[1] == "-speakerDiarizationScriptEval":
			dir = argv[2]
			listOfLDAs = [int(l) for l in argv[3::]]
			aS.speakerDiarizationEvaluateScript(dir, listOfLDAs)

	elif argv[1] == '-thumbnail':			# music thumbnailing (OK)
			if len(argv)==4:	
				inputFile = argv[2]
				stWindow = 1.0
				stStep = 1.0
				if not os.path.isfile(inputFile):
					raise Exception("Input audio file not found!")

				[Fs, x] = audioBasicIO.readAudioFile(inputFile)						# read file
				if Fs == -1:	# could not read file
					return
				try:
					thumbnailSize = float(argv[3])
				except ValueError:
					print "Thumbnail size must be a float (in seconds)"
					return 
				[A1, A2, B1, B2, Smatrix] = aS.musicThumbnailing(x, Fs, stWindow, stStep, thumbnailSize)	# find thumbnail endpoints			

				# write thumbnails to WAV files:
				thumbnailFileName1 = inputFile.replace(".wav","_thumb1.wav")
				thumbnailFileName2 = inputFile.replace(".wav","_thumb2.wav")
				wavfile.write(thumbnailFileName1, Fs, x[int(Fs*A1):int(Fs*A2)])
				wavfile.write(thumbnailFileName2, Fs, x[int(Fs*B1):int(Fs*B2)])
				print "1st thumbnail (stored in file {0:s}): {1:4.1f}sec -- {2:4.1f}sec".format(thumbnailFileName1, A1, A2)
				print "2nd thumbnail (stored in file {0:s}): {1:4.1f}sec -- {2:4.1f}sec".format(thumbnailFileName2, B1, B2)

				# Plot self-similarity matrix:
				fig = plt.figure()
				ax = fig.add_subplot(111, aspect='auto')
				plt.imshow(Smatrix)
				# Plot best-similarity diagonal:
				Xcenter = (A1/stStep + A2/stStep) / 2.0
				Ycenter = (B1/stStep + B2/stStep) / 2.0


				e1 = matplotlib.patches.Ellipse((Ycenter, Xcenter), thumbnailSize * 1.4, 3,
			             angle=45, linewidth=3, fill=False)
				ax.add_patch(e1)

				plt.plot([B1, Smatrix.shape[0]], [A1, A1], color='k', linestyle='--', linewidth=2)
				plt.plot([B2, Smatrix.shape[0]], [A2, A2], color='k', linestyle='--', linewidth=2)
				plt.plot([B1, B1], [A1, Smatrix.shape[0]], color='k', linestyle='--', linewidth=2)
				plt.plot([B2, B2], [A2, Smatrix.shape[0]], color='k', linestyle='--', linewidth=2)

				plt.xlim([0, Smatrix.shape[0]])
				plt.ylim([Smatrix.shape[1], 0])



				ax.yaxis.set_label_position("right")
				ax.yaxis.tick_right()


				plt.xlabel('frame no')
				plt.ylabel('frame no')
				plt.title('Self-similarity matrix')

				plt.show()

			else: 
				print "Error.\nSyntax: " + argv[0] + " -thumbnail <filename> <thumbnailsize(seconds)>"