Exemplo n.º 1
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def featureExtractionFileWrapper(wav_file, out_file, mt_win, mt_step,
                                 st_win, st_step):
    if not os.path.isfile(wav_file):
        raise Exception("Input audio file not found!")

    aF.mtFeatureExtractionToFile(wav_file, mt_win, mt_step, st_win,
                                 st_step, out_file, True, True, True)
Exemplo n.º 2
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def featureExtractionFileWrapper(wav_file, out_file, mt_win, mt_step, st_win,
                                 st_step):
    if not os.path.isfile(wav_file):
        raise Exception("Input audio file not found!")

    aF.mtFeatureExtractionToFile(wav_file, mt_win, mt_step, st_win, st_step,
                                 out_file, True, True, True)
Exemplo n.º 3
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def feature_extraction(filename):
    wavFileName = filename.replace('.mp3','.wav')
    name = filename.replace('.mp3','')
    command = 'avconv -i ' + filename + ' ' + wavFileName
    os.system(command)
    audioFeatureExtraction.mtFeatureExtractionToFile(wavFileName,2.0, 2.0, 0.050, 0.050,name,storeStFeatures=True,storeToCSV=True)
    return name + '.csv'
Exemplo n.º 4
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def featureExtractionFileWrapper(wavFileName, outFile, mtWin, mtStep, stWin,
                                 stStep):
    if not os.path.isfile(wavFileName):
        raise Exception("Input audio file not found!")

    aF.mtFeatureExtractionToFile(wavFileName, mtWin, mtStep, stWin, stStep,
                                 outFile, True, True, True)
Exemplo n.º 5
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    def extract_mid_features(input_file):
        class_names = [os.path.basename(input_file)]
        features = []
        fs, x = readAudioFile(input_file)
        x = stereo2mono(x)
        mt_size, mt_step, st_win, st_step = 1, 0.4, 0.025, 0.010
        [mt_feats, st_feats, _] = mtFeatureExtraction(x, fs, mt_size * fs,
                                                      mt_step * fs,
                                                      round(st_win * fs),
                                                      round(st_step * fs))

        mtFeatureExtractionToFile(input_file, mt_size, mt_step, st_win,
                                  st_step, input_file, False, True, True)
        return mt_feats, st_feats, _
Exemplo n.º 6
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def extract_features(filename):
    ''' Extracts features from a file of audio data
		Parameters:
			filename
				str, the name of the file to extract features from
		Returns:
			filename to extracted features
	'''
    # base name of output file
    output_filename = "computed_features_file"
    # base list of feature names for labelng with json
    feature_names = ("Zero_Crosing_Rate", "Energy", "Entropy_of_Energy",
                     "Spectral_Centroid", "Spectral_Spread",
                     "Spectral_Entropy", "Spectral_Flux", "Spectral_Rollof",
                     "MFCC_1", "MFCC_2", "MFCC_3", "MFCC_4", "MFCC_5",
                     "MFCC_6", "MFCC_7", "MFCC_8", "MFCC_9", "MFCC_10",
                     "MFCC_11", "MFCC_12", "MFCC_13", "Chroma_Vector_1",
                     "Chroma_Vector_2", "Chroma_Vector_3", "Chroma_Vector_4",
                     "Chroma_Vector_5", "Chroma_Vector_6", "Chroma_Vector_7",
                     "Chroma_Vector_8", "Chroma_Vector_9", "Chroma_Vector_10",
                     "Chroma_Vector_11", "Chroma_Vector_12",
                     "Chroma_Deviation")

    # arguments 2 and 3 are not in use
    # argument 7 and 8 store short term features and enable csv output respectively
    afe.mtFeatureExtractionToFile(filename, 1, 1, .05, .025, output_filename,
                                  True, True, False)

    # convert to json format
    csv_features = open(output_filename + '_st.csv', 'r')

    # add feature names as json keys
    json_features = []
    reader = csv.DictReader(csv_features, feature_names)
    # add rows to json list
    for row in reader:
        json_features.append(row)

    csv_features.close()

    # remove unnecessary files
    os.remove(output_filename + ".csv")
    os.remove(output_filename + ".npy")
    os.remove(output_filename + "_st.csv")
    os.remove(output_filename + "_st.npy")

    # return the list of formatted features
    return json_features
Exemplo n.º 7
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    def Audio_Feature_Extraction_Extract_Directory(self):

        pathToSaveFiles = QFileDialog.getExistingDirectory(
            self.somethingToPass, "Select Directory to Save Files")

        i = 0

        path = self.Audio_Feature_Extraction_DirectoryPath.text()
        extractionList = []
        nameList = []
        tempHold = []

        for root, directs, files in os.walk(path):
            for x in files:
                extractionList.append(root + "/" + x)
                nameList.append(x)

        for name in nameList:
            x = name.split('.')
            tempHold.append(x[0])

        nameList = tempHold

        if self.Audio_Feature_Extraction_DirectoryWindowTerm.currentText(
        ) == "ShortTerm":

            for audio in extractionList:
                [Fs, x] = audioBasicIO.readAudioFile(audio)
                print(audio)
                stFeatures = audioFeatureExtraction.stFeatureExtraction(
                    x, Fs,
                    float(self.Audio_Feature_Extraction_DirectoryWindowSize.
                          text()) * Fs,
                    float(
                        self.Audio_Feature_Extraction_DirectoryStepSize.text())
                    * Fs)
                #I think the files are overwriting each other.  I am getting
                #299 files but it should be 5134.  I changed the namelist[i] to
                #just i
                numpy.savetxt(pathToSaveFiles + "/" + str(i) + ".csv",
                              stFeatures,
                              delimiter=',')

                i += 1

            QMessageBox.about(self.somethingToPass, "Files Created",
                              "Files have been saved as CSV files.")

        else:
            for x in extractionList:

                audioFeatureExtraction.mtFeatureExtractionToFile(
                    x,
                    float(self.
                          Audio_Feature_Extraction_DirectorymidTermWindowSize.
                          text()),
                    float(
                        self.
                        Audio_Feature_Extraction_DirectorymidTermWindowStepSize
                        .text()),
                    float(self.Audio_Feature_Extraction_DirectoryWindowSize.
                          text()),
                    float(self.Audio_Feature_Extraction_DirectoryStepSize.text(
                    )),
                    pathToSaveFiles + "/" + nameList[i],
                    storeStFeatures=True,
                    storeToCSV=True,
                    PLOT=False)
                i += 1

            QMessageBox.about(
                self.somethingToPass, "Files Created",
                "Files have been saved as CSV files and .npy files. There ")
Exemplo n.º 8
0
    def Audio_Feature_Extraction_Extract(self):
        if self.Audio_Feature_Extraction_SingleFileWindowTerm.currentText(
        ) == "ShortTerm":
            [Fs, x] = audioBasicIO.readAudioFile(
                self.Audio_Feature_Extraction_SingleFilePath.text())
            stFeatures = audioFeatureExtraction.stFeatureExtraction(
                x, Fs,
                float(
                    self.Audio_Feature_Extraction_SingleFileWindowSize.text())
                * Fs,
                float(self.Audio_Feature_Extraction_SingleFileStepSize.text())
                * Fs)

            options = QFileDialog.Options()
            options |= QFileDialog.DontUseNativeDialog
            fileName, _ = QFileDialog.getSaveFileName(self.somethingToPass,
                                                      "Where to save?",
                                                      "",
                                                      "CSV files (*.csv)",
                                                      options=options)

            numpy.savetxt(fileName + ".csv", stFeatures, delimiter=',')
            QMessageBox.about(self.somethingToPass, "Files Created",
                              "File has been saved as CSV file")
            ## Let the User specify how many features and what features to see eventually.
#==============================================================================
#             if self.Audio_Feature_Extraction_SingleFileDataVisualisation.isChecked():
#                 stFeatures = audioFeatureExtraction.stFeatureExtraction(x, Fs, 0.050*Fs, 0.025*Fs)
#
#                 labels = ["Zero Crossing Rate", "Energy", "Entropy of Energy", "Spectral Centroid",
#                           "Spectral Spread", "Spectral Entropy", "Spectral Flux", "Spectral Rolloff",
#                           "MFCC 1", "MFCC 2", "MFCC 3", "MFCC 4",
#                           "MFCC 5", "MFCC 6", "MFCC 7", "MFCC 8",
#                           "MFCC 9", "MFCC 10", "MFCC 11", "MFCC 12", "MFCC 13",
#                           "Chroma Vector 1", "Chroma Vector 2", "Chroma Vector 3", "Chroma Vector 4",
#                           "Chroma Vector 5", "Chroma Vector 6", "Chroma Vector 7", "Chroma Vector 8",
#                           "Chroma Vector 9", "Chroma Vector 10", "Chroma Vector 11","Chroma Vector 12", "Chroma Deviation"]
#
#
#                 for x in range(0, len(labels)-1):
#                     plt.subplot(34,1,x+1); plt.plot(stFeatures[x,:]); plt.xlabel('Frame no'); plt.ylabel(labels[x])
#
#                 plt.show()
#
#==============================================================================

        else:
            options = QFileDialog.Options()
            options |= QFileDialog.DontUseNativeDialog
            fileName, _ = QFileDialog.getSaveFileName(self.somethingToPass,
                                                      "Where to save?",
                                                      "",
                                                      "CSV files (*.csv)",
                                                      options=options)

            audioFeatureExtraction.mtFeatureExtractionToFile(
                self.Audio_Feature_Extraction_SingleFilePath.text(),
                float(
                    self.Audio_Feature_Extraction_SingleFilemidTermWindowSize.
                    text()),
                float(self.
                      Audio_Feature_Extraction_SingleFilemidTermWindowStepSize.
                      text()),
                float(
                    self.Audio_Feature_Extraction_SingleFileWindowSize.text()),
                float(self.Audio_Feature_Extraction_SingleFileStepSize.text()),
                fileName,
                storeStFeatures=True,
                storeToCSV=True,
                PLOT=False)

            QMessageBox.about(
                self.somethingToPass, "Files Created",
                "Files have been saved as CSV files and .npy files")