def deconv_a_file(filename):

	song_id = filename.split('.')[0]

	path_out_here = path_results + song_id + '/'
	# path_img_here = path_results + song_id + '_img/'
	SRC = np.load(path_SRC + filename)
	if os.path.exists(path_out_here):
		print '%s might be done already, I skip this.' % song_id
		print 'remove %s and %s to proceed.' % (path_out_here, path_img_here)
	if not os.path.exists(path_out_here):
		os.makedirs(path_out_here)	
	# if not os.path.exists(path_img_here):
	# 	os.makedirs(path_img_here)
	filename_out = '%s_a_original.wav' % (song_id)	
	librosa.output.write_wav(path_out_here + filename_out, librosa.istft(SRC, hop_length=N_FFT/2), 
								sr=SAMPLE_RATE, 
								norm=True)

	for depth in depths:	
		print '--- deconve! ---'
		deconvedMASKS = auralise.get_deconve_mask(W[:depth], layer_names, SRC, depth) # size can be smaller than SRC due to downsampling

		print 'result; %d masks with size of %d, %d' % deconvedMASKS.shape

		for deconved_feature_ind, deconvedMASK_here in enumerate(deconvedMASKS):

			MASK = np.zeros(SRC.shape)
			MASK[0:deconvedMASK_here.shape[0], 0:deconvedMASK_here.shape[1]] = deconvedMASK_here
			deconvedSRC = np.multiply(SRC, MASK)

			filename_out = '%s_deconved_from_depth_%d_feature_%d.wav' % (song_id, depth, deconved_feature_ind)
			librosa.output.write_wav(path_out_here + filename_out, librosa.istft(deconvedSRC, hop_length=N_FFT/2), 
								sr=SAMPLE_RATE, 
								norm=True)
Example #2
0
def deconv_a_file(filename):

    song_id = filename.split('.')[0]

    path_out_here = path_results + song_id + '/'
    # path_img_here = path_results + song_id + '_img/'
    SRC = np.load(path_SRC + filename)
    if os.path.exists(path_out_here):
        print '%s might be done already, I skip this.' % song_id
        print 'remove %s and %s to proceed.' % (path_out_here, path_img_here)
    if not os.path.exists(path_out_here):
        os.makedirs(path_out_here)
    # if not os.path.exists(path_img_here):
    # 	os.makedirs(path_img_here)
    filename_out = '%s_a_original.wav' % (song_id)
    librosa.output.write_wav(path_out_here + filename_out,
                             librosa.istft(SRC, hop_length=N_FFT / 2),
                             sr=SAMPLE_RATE,
                             norm=True)

    for depth in depths:
        print '--- deconve! ---'
        deconvedMASKS = auralise.get_deconve_mask(
            W[:depth], layer_names, SRC,
            depth)  # size can be smaller than SRC due to downsampling

        print 'result; %d masks with size of %d, %d' % deconvedMASKS.shape

        for deconved_feature_ind, deconvedMASK_here in enumerate(
                deconvedMASKS):

            MASK = np.zeros(SRC.shape)
            MASK[0:deconvedMASK_here.shape[0],
                 0:deconvedMASK_here.shape[1]] = deconvedMASK_here
            deconvedSRC = np.multiply(SRC, MASK)

            filename_out = '%s_deconved_from_depth_%d_feature_%d.wav' % (
                song_id, depth, deconved_feature_ind)
            librosa.output.write_wav(path_out_here + filename_out,
                                     librosa.istft(deconvedSRC,
                                                   hop_length=N_FFT / 2),
                                     sr=SAMPLE_RATE,
                                     norm=True)
Example #3
0
	# deconve
	depths = [5,4,3,2,1]
	for filename in filenames_SRC:
		song_id = filename.split('.')[0]
		SRC = np.load(path_SRC + filename)
		filename_out = '%s_a_original.wav' % (song_id)	
		if not os.path.exists(path_SRC + song_id):
			os.makedirs(path_src + song_id)	
		if not os.path.exists(path_SRC + song_id + '_img'):
			os.makedirs(path_src + song_id + '_img')
		librosa.output.write_wav(path_src+ song_id + '/' + filename_out, librosa.istft(SRC, hop_length=N_FFT/2), sr=SAMPLE_RATE, norm=True)

		for depth in depths:
	
			print '--- deconve! ---'
			deconvedMASKS = auralise.get_deconve_mask(W[:depth], layer_names, SRC, depth) # size can be smaller than SRC due to downsampling

			print 'result; %d masks with size of %d, %d' % deconvedMASKS.shape
			for deconved_feature_ind, deconvedMASK_here in enumerate(deconvedMASKS):
				MASK = np.zeros(SRC.shape)
				MASK[0:deconvedMASK_here.shape[0], 0:deconvedMASK_here.shape[1]] = deconvedMASK_here
				deconvedSRC = np.multiply(SRC, MASK)

				filename_out = '%s_deconved_from_depth_%d_feature_%d.wav' % (song_id, depth, deconved_feature_ind)
				librosa.output.write_wav(path_src+ song_id + '/' + filename_out, librosa.istft(deconvedSRC, hop_length=N_FFT/2), SAMPLE_RATE, norm=True)
				filename_img_out = 'spectrogram_%s_from_depth_%d_feature_%d.png' % (song_id, depth, deconved_feature_ind)
				imsave(path_src+song_id + '_img' + '/' + filename_img_out , np.flipud(np.multiply(np.abs(SRC), MASK)))

				filename_img_out = 'filter_for_%s_from_depth_%d_feature_%d.png' % (song_id, depth, deconved_feature_ind)
				imsave(path_src+song_id + '_img' + '/' + filename_img_out , np.flipud(MASK))
				
Example #4
0
        song_id = filename.split(".")[0]
        SRC = np.load(path_SRC + filename)
        filename_out = "%s_a_original.wav" % (song_id)
        if not os.path.exists(path_SRC + song_id):
            os.makedirs(path_src + song_id)
        if not os.path.exists(path_SRC + song_id + "_img"):
            os.makedirs(path_src + song_id + "_img")
        librosa.output.write_wav(
            path_src + song_id + "/" + filename_out, librosa.istft(SRC, hop_length=N_FFT / 2), sr=SAMPLE_RATE, norm=True
        )

        for depth in depths:

            print "--- deconve! ---"
            deconvedMASKS = auralise.get_deconve_mask(
                W[:depth], layer_names, SRC, depth
            )  # size can be smaller than SRC due to downsampling

            print "result; %d masks with size of %d, %d" % deconvedMASKS.shape
            for deconved_feature_ind, deconvedMASK_here in enumerate(deconvedMASKS):
                MASK = np.zeros(SRC.shape)
                MASK[0 : deconvedMASK_here.shape[0], 0 : deconvedMASK_here.shape[1]] = deconvedMASK_here
                deconvedSRC = np.multiply(SRC, MASK)

                filename_out = "%s_deconved_from_depth_%d_feature_%d.wav" % (song_id, depth, deconved_feature_ind)
                librosa.output.write_wav(
                    path_src + song_id + "/" + filename_out,
                    librosa.istft(deconvedSRC, hop_length=N_FFT / 2),
                    SAMPLE_RATE,
                    norm=True,
                )