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)
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)
# 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))
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,
)