denoise.py
def deNoise(self, outputFile):
        """
        De-noising function that reads the audio signal in chunks and processes
        and writes to the output file efficiently.

        VISU Shrink is used to generate the noise threshold

        Parameters
        ----------
        outputFile : str
            de-noised file name

        """
        info = soundfile.info(self.__inputFile)  # getting info of the audio
        rate = info.samplerate

        with soundfile.SoundFile(outputFile, "w", samplerate=rate, channels=info.channels) as of:
            for block in tqdm(soundfile.blocks(self.__inputFile, int(rate * info.duration * 0.10))):
                coefficients = pywt.wavedec(block, 'db4', mode='per', level=2)

                #  getting variance of the input signal
                sigma = mad(coefficients[- 1])

                # VISU Shrink thresholding by applying the universal threshold proposed by Donoho and Johnstone
                thresh = sigma * np.sqrt(2 * np.log(len(block)))

                # thresholding using the noise threshold generated
                coefficients[1:] = (pywt.threshold(i, value=thresh, mode='soft') for i in coefficients[1:])

                # getting the clean signal as in original form and writing to the file
                clean = pywt.waverec(coefficients, 'db4', mode='per')
                of.write(clean)

        De-noising function that reads the audio signal in chunks and processes
        and writes to the output file efficiently.

        VISU Shrink is used to generate the noise threshold

        Parameters
        ----------
        outputFile : str
            de-noised file name

        
De-noising function that reads the audio signal in chunks and processes
and writes to the output file efficiently.

VISU Shrink is used to generate the noise threshold

Parameters

outputFile : str
de-noised file name
De - noising function that reads the audio signal in chunks and processes and writes to the output file efficiently . VISU Shrink is used to generate the noise threshold Parameters outputFile : str de - noised file name
def deNoise(self, outputFile):
        info = soundfile.info(self.__inputFile)  
        rate = info.samplerate
        with soundfile.SoundFile(outputFile, "w", samplerate=rate, channels=info.channels) as of:
            for block in tqdm(soundfile.blocks(self.__inputFile, int(rate * info.duration * 0.10))):
                coefficients = pywt.wavedec(block, 'db4', mode='per', level=2)
                sigma = mad(coefficients[- 1])
                thresh = sigma * np.sqrt(2 * np.log(len(block)))
                coefficients[1:] = (pywt.threshold(i, value=thresh, mode='soft') for i in coefficients[1:])
                clean = pywt.waverec(coefficients, 'db4', mode='per')
                of.write(clean)
def deNoise ( self , outputFile ) : info = soundfile . info ( self . __inputFile ) rate = info . samplerate with soundfile . SoundFile ( outputFile , "w" , samplerate = rate , channels = info . channels ) as of : for block in tqdm ( soundfile . blocks ( self . __inputFile , int ( rate * info . duration * 0.10 ) ) ) : coefficients = pywt . wavedec ( block , 'db4' , mode = 'per' , level = 2 ) sigma = mad ( coefficients [ - 1 ] ) thresh = sigma * np . sqrt ( 2 * np . log ( len ( block ) ) ) coefficients [ 1 : ] = ( pywt . threshold ( i , value = thresh , mode = 'soft' ) for i in coefficients [ 1 : ] ) clean = pywt . waverec ( coefficients , 'db4' , mode = 'per' ) of . write ( clean )
De-noising function that reads the audio signal in chunks and processes
and writes to the output file efficiently.
De - noising function that reads the audio signal in chunks and processes and writes to the output file efficiently .
"""
        De-noising function that reads the audio signal in chunks and processes
        and writes to the output file efficiently.

        VISU Shrink is used to generate the noise threshold

        Parameters
        ----------
        outputFile : str
            de-noised file name

        """ # getting info of the audio #  getting variance of the input signal # VISU Shrink thresholding by applying the universal threshold proposed by Donoho and Johnstone # thresholding using the noise threshold generated # getting the clean signal as in original form and writing to the file
self
outputFile