def noise_resnet_block(inputs, kernel_size, stride, n_fmaps, scope_name):
'''
Embedding network
:param inputs : context input
:return out : embedding vector represents the context information
'''
# The transformation path
path1 = conv2d(inputs, kernel_size, [1] + stride + [1], n_fmaps, FLAGS.w_std, FLAGS.b_init, False, 'SAME', scope_name + '_conv1')
path1 = batch_norm(istrain, path1, scope_name + '_conv1')
path1 = tf.nn.relu(path1)
path1 = conv2d(path1, kernel_size, [1, 1, 1, 1], n_fmaps, FLAGS.w_std, FLAGS.b_init, True, 'SAME', scope_name + '_conv2')
# The identity path
n_input_channels = inputs.shape.as_list()[3]
if n_input_channels == n_fmaps:
path2 = inputs
else:
path2 = conv2d(inputs, [1, 1], [1] + stride + [1], n_fmaps, FLAGS.w_std, FLAGS.b_init, True, 'SAME', scope_name + '_transform')
# Add and return
assert path1.shape.as_list() == path2.shape.as_list()
out = path1 + path2
out = batch_norm(istrain, out, scope_name + '_addition')
out = tf.nn.relu(out)
return out
def cont_embed(n, out_dim, scope_name): out = tf.constant(list(range(0, n)), dtype=tf.float32) # [n] out = tf.reshape(out, [n, 1]) # [n, 1] out = dense(out, 50, FLAGS.w_std, 0.0, False, scope_name + '_dense1') # [n, 50] out = batch_norm(istrain, out, scope_name + scope_name + '_dense1') out = tf.nn.relu(out) out = dense(out, 50, FLAGS.w_std, 0.0, False, scope_name + '_dense2') # [n, 50] out = batch_norm(istrain, out, scope_name + scope_name + '_dense2') out = tf.nn.relu(out) out = dense(out, out_dim, 0.0, 0.0, False, scope_name + '_dense3') # [n, out_dim] return out
def model(inputs, istrain):
target, mixed, mixedph, targetph, pos, posph, neg, negph, noiseposcontext, noisenegcontext, location, cleanpath, noisepospath, noisenegpath, snr_pos, snr_neg = inputs
nfeat = target.shape[2].value
def noise_resnet_block(inputs, kernel_size, stride, n_fmaps, scope_name):
'''
Embedding network
:param inputs : context input
:return out : embedding vector represents the context information
'''
# The transformation path
path1 = conv2d(inputs, kernel_size, [1] + stride + [1], n_fmaps, FLAGS.w_std, FLAGS.b_init, False, 'SAME', scope_name + '_conv1')
path1 = batch_norm(istrain, path1, scope_name + '_conv1')
path1 = tf.nn.relu(path1)
path1 = conv2d(path1, kernel_size, [1, 1, 1, 1], n_fmaps, FLAGS.w_std, FLAGS.b_init, True, 'SAME', scope_name + '_conv2')
# The identity path
n_input_channels = inputs.shape.as_list()[3]
if n_input_channels == n_fmaps:
path2 = inputs
else:
path2 = conv2d(inputs, [1, 1], [1] + stride + [1], n_fmaps, FLAGS.w_std, FLAGS.b_init, True, 'SAME', scope_name + '_transform')
# Add and return
assert path1.shape.as_list() == path2.shape.as_list()
out = path1 + path2
out = batch_norm(istrain, out, scope_name + '_addition')
out = tf.nn.relu(out)
return out
def resnet_block(inputs, noiseposemb, noisenegemb, kernel_size, stride, n_fmaps, scope_name):
'''
Residual block to process noisy signals, with injection of embedding vectors
:param inputs : input feature maps
:param noiseposemb : positive embedding vector
:param noisenegemb : negative embedding vector
:param n_fmaps : number of channels
:return out : output feature maps
'''
def cont_embed(n, out_dim, scope_name):
out = tf.constant(list(range(0, n)), dtype=tf.float32) # [n]
out = tf.reshape(out, [n, 1]) # [n, 1]
out = dense(out, 50, FLAGS.w_std, 0.0, False, scope_name + '_dense1') # [n, 50]
out = batch_norm(istrain, out, scope_name + scope_name + '_dense1')
out = tf.nn.relu(out)
out = dense(out, 50, FLAGS.w_std, 0.0, False, scope_name + '_dense2') # [n, 50]
out = batch_norm(istrain, out, scope_name + scope_name + '_dense2')
out = tf.nn.relu(out)
out = dense(out, out_dim, 0.0, 0.0, False, scope_name + '_dense3') # [n, out_dim]
return out
def process_noise_t_f(match_to, scope_name):
n_fmaps = match_to.shape[3].value
# Project the noise to fit the conv
noisepos_proj = dense(noiseposemb, n_fmaps, 0.0, 0.0, True, scope_name + '_noise_pos_emb') # [mb, n_fmaps]
noisepos_proj = tf.expand_dims(noisepos_proj, 1)
noisepos_proj = tf.expand_dims(noisepos_proj, 1) # [mb, 1, 1, n_fmaps]
noiseneg_proj = dense(noisenegemb, n_fmaps, 0.0, 0.0, True, scope_name + '_noise_neg_emb') # [mb, n_fmaps]
noiseneg_proj = tf.expand_dims(noiseneg_proj, 1)
noiseneg_proj = tf.expand_dims(noiseneg_proj, 1) # [mb, 1, 1, n_fmaps]
# Get the time and frequency embedding
ts, fs = match_to.shape[1].value, match_to.shape[2].value
tout = cont_embed(ts, n_fmaps, scope_name + '_temb') # [ts, n_fmaps]
tout = tf.expand_dims(tout, 1)
tout = tf.expand_dims(tout, 0) # [1, time, 1, n_fmaps]
fout = cont_embed(fs, n_fmaps, scope_name + '_femb') # [fs, n_fmaps]
fout = tf.expand_dims(fout, 0)
fout = tf.expand_dims(fout, 0) # [1, 1, freq, n_fmaps]
return noisepos_proj, noiseneg_proj, tout, fout
# The transformation path
path1 = conv2d(inputs, [kernel_size, kernel_size], [1, stride, stride, 1],
n_fmaps, FLAGS.w_std, FLAGS.b_init, False,
'SAME', scope_name + '_conv1') # [mb, time, freq, n_fmaps]
noisepos_proj1, noiseneg_proj1, tout1, fout1 = process_noise_t_f(path1, scope_name + '_conv1')
path1 = path1 + noisepos_proj1 + noiseneg_proj1 + tout1 + fout1
path1 = batch_norm(istrain, path1, scope_name + '_conv1')
path1 = tf.nn.relu(path1)
path1 = conv2d(path1, [kernel_size, kernel_size], [1,1,1,1], n_fmaps, FLAGS.w_std, FLAGS.b_init, True, 'SAME', scope_name + '_conv2')
noisepos_proj2, noiseneg_proj2, tout2, fout2 = process_noise_t_f(path1, scope_name + '_conv2')
path1 = path1 + noisepos_proj2 + noiseneg_proj2 + tout2 + fout2
# The identity path
n_input_channels = inputs.shape.as_list()[3]
if n_input_channels == n_fmaps:
path2 = inputs
else:
path2 = conv2d(inputs, [1, 1], [1, stride, stride, 1], n_fmaps, FLAGS.w_std, FLAGS.b_init, True, 'SAME', scope_name + '_transform')
# Add and return
assert path1.shape.as_list() == path2.shape.as_list()
out = path1 + path2
out = batch_norm(istrain, out, scope_name + '_addition')
out = tf.nn.relu(out)
return out
# The positive noise embedding
with tf.variable_scope('embedding'):
nout = None
nout = noiseposcontext # [mb, noise frames, 201]
nout = tf.expand_dims(nout, 3)
nout = noise_resnet_block(nout, [8, 4], [3, 2], 64, 'noise_resblock1_1') # [mb, noise frames, 201, 64]
nout = noise_resnet_block(nout, [8, 4], [3, 2], 128, 'noise_resblock2_1') # [mb, noise frames / 2, 201 / 2, 64]
nout = noise_resnet_block(nout, [4, 4], [1, 1], 256, 'noise_resblock3_1') # [mb, noise frames / 4, 201 / 4, 64]
nout = noise_resnet_block(nout, [4, 4], [1, 2], 512, 'noise_resblock4_1') # [mb, noise frames / 8, 201 / 8, 512]
nout = tf.nn.avg_pool(nout, [1, nout.shape[1].value, nout.shape[2].value, 1], [1, 1, 1, 1], 'VALID') # [mb, 1, 1, 512]
assert nout.shape.as_list()[1:3] == [1, 1]
noiseposemb = nout[:, 0, 0, :] # [mb, 512]
# The negative noise embedding
with tf.variable_scope('embedding', reuse=True):
nout = None
nout = noisenegcontext # [mb, noise frames, 201]
nout = tf.expand_dims(nout, 3)
nout = noise_resnet_block(nout, [8, 4], [3, 2], 64, 'noise_resblock1_1') # [mb, noise frames, 201, 64]
nout = noise_resnet_block(nout, [8, 4], [3, 2], 128, 'noise_resblock2_1') # [mb, noise frames / 2, 201 / 2, 64]
nout = noise_resnet_block(nout, [4, 4], [1, 1], 256, 'noise_resblock3_1') # [mb, noise frames / 4, 201 / 4, 64]
nout = noise_resnet_block(nout, [4, 4], [1, 2], 512, 'noise_resblock4_1') # [mb, noise frames / 8, 201 / 8, 512]
nout = tf.nn.avg_pool(nout, [1, nout.shape[1].value, nout.shape[2].value, 1], [1, 1, 1, 1], 'VALID') # [mb, 1, 1, 512]
assert nout.shape.as_list()[1:3] == [1, 1]
noisenegemb = nout[:, 0, 0, :] # [mb, 512]
# Processing the mixed signal
out = mixed # [mb, context frames, 201]
out = tf.expand_dims(out, 3)
out = resnet_block(out, noiseposemb, noisenegemb, 4, 1, 64, 'resblock1_1')
out = resnet_block(out, noiseposemb, noisenegemb, 4, 1, 64, 'resblock1_2')
out = resnet_block(out, noiseposemb, noisenegemb, 4, 2, 128, 'resblock2_1')
out = resnet_block(out, noiseposemb, noisenegemb, 4, 1, 128, 'resblock2_2')
out = resnet_block(out, noiseposemb, noisenegemb, 3, 2, 256, 'resblock3_1')
out = resnet_block(out, noiseposemb, noisenegemb, 3, 1, 256, 'resblock3_2')
out = resnet_block(out, noiseposemb, noisenegemb, 3, 2, 512, 'resblock4_1')
out = resnet_block(out, noiseposemb, noisenegemb, 3, 1, 512, 'resblock4_2') # [mb, context frames / 8, 201 / 8, 512]
# final layers
out = conv2d(out, [out.shape[1].value, 1], [1, 1, 1, 1],
512, FLAGS.w_std, FLAGS.b_init, False,
'VALID', 'last_conv') # [mb, 1, 201 / 8, 512]
out = batch_norm(istrain, out, 'last_conv')
out = tf.nn.relu(out)
out = flatten(out) # [mb, (201 / 8) * 512]
out = dense(out, nfeat, 0.0, 0.0, True, 'last_dense') # [mb, 201]
mixed_central = mixed[:, FLAGS.window_frames // 2, :] # [mb, 201]
pos_central = pos[:, FLAGS.window_frames // 2, :] # [mb, 201]
neg_central = neg[:, FLAGS.window_frames // 2, :] # [mb, 201]
denoised = mixed_central + out # [mb, 201]
# Loss
se = tf.square(denoised - target[:, 0, :]) # [mb, 201]
imp_factor = np.linspace(2, 1, nfeat, dtype=np.float32).reshape((1, nfeat))
example_loss = tf.reduce_mean(se * tf.constant(imp_factor), axis=1)
loss = tf.reduce_mean(example_loss)
monitors = {'loss': loss}
outputs = {'loss': example_loss, 'mixed': mixed_central, 'denoised': denoised, 'target': target[:, 0, :],
'mixedph': mixedph[:, 0, :], 'targetph': targetph[:, 0, :], 'pos': pos_central, 'neg': neg_central, 'posph': posph[:, 0, :], 'negph': negph[:, 0, :], 'location': location, 'cleanpath': cleanpath,
'noisepospath': noisepospath, 'noisenegpath': noisenegpath, 'snr_pos': snr_pos, 'snr_neg': snr_neg}
return loss, monitors, outputs
def resnet_block(inputs, noiseposemb, noisenegemb, kernel_size, stride, n_fmaps, scope_name):
'''
Residual block to process noisy signals, with injection of embedding vectors
:param inputs : input feature maps
:param noiseposemb : positive embedding vector
:param noisenegemb : negative embedding vector
:param n_fmaps : number of channels
:return out : output feature maps
'''
def cont_embed(n, out_dim, scope_name):
out = tf.constant(list(range(0, n)), dtype=tf.float32) # [n]
out = tf.reshape(out, [n, 1]) # [n, 1]
out = dense(out, 50, FLAGS.w_std, 0.0, False, scope_name + '_dense1') # [n, 50]
out = batch_norm(istrain, out, scope_name + scope_name + '_dense1')
out = tf.nn.relu(out)
out = dense(out, 50, FLAGS.w_std, 0.0, False, scope_name + '_dense2') # [n, 50]
out = batch_norm(istrain, out, scope_name + scope_name + '_dense2')
out = tf.nn.relu(out)
out = dense(out, out_dim, 0.0, 0.0, False, scope_name + '_dense3') # [n, out_dim]
return out
def process_noise_t_f(match_to, scope_name):
n_fmaps = match_to.shape[3].value
# Project the noise to fit the conv
noisepos_proj = dense(noiseposemb, n_fmaps, 0.0, 0.0, True, scope_name + '_noise_pos_emb') # [mb, n_fmaps]
noisepos_proj = tf.expand_dims(noisepos_proj, 1)
noisepos_proj = tf.expand_dims(noisepos_proj, 1) # [mb, 1, 1, n_fmaps]
noiseneg_proj = dense(noisenegemb, n_fmaps, 0.0, 0.0, True, scope_name + '_noise_neg_emb') # [mb, n_fmaps]
noiseneg_proj = tf.expand_dims(noiseneg_proj, 1)
noiseneg_proj = tf.expand_dims(noiseneg_proj, 1) # [mb, 1, 1, n_fmaps]
# Get the time and frequency embedding
ts, fs = match_to.shape[1].value, match_to.shape[2].value
tout = cont_embed(ts, n_fmaps, scope_name + '_temb') # [ts, n_fmaps]
tout = tf.expand_dims(tout, 1)
tout = tf.expand_dims(tout, 0) # [1, time, 1, n_fmaps]
fout = cont_embed(fs, n_fmaps, scope_name + '_femb') # [fs, n_fmaps]
fout = tf.expand_dims(fout, 0)
fout = tf.expand_dims(fout, 0) # [1, 1, freq, n_fmaps]
return noisepos_proj, noiseneg_proj, tout, fout
# The transformation path
path1 = conv2d(inputs, [kernel_size, kernel_size], [1, stride, stride, 1],
n_fmaps, FLAGS.w_std, FLAGS.b_init, False,
'SAME', scope_name + '_conv1') # [mb, time, freq, n_fmaps]
noisepos_proj1, noiseneg_proj1, tout1, fout1 = process_noise_t_f(path1, scope_name + '_conv1')
path1 = path1 + noisepos_proj1 + noiseneg_proj1 + tout1 + fout1
path1 = batch_norm(istrain, path1, scope_name + '_conv1')
path1 = tf.nn.relu(path1)
path1 = conv2d(path1, [kernel_size, kernel_size], [1,1,1,1], n_fmaps, FLAGS.w_std, FLAGS.b_init, True, 'SAME', scope_name + '_conv2')
noisepos_proj2, noiseneg_proj2, tout2, fout2 = process_noise_t_f(path1, scope_name + '_conv2')
path1 = path1 + noisepos_proj2 + noiseneg_proj2 + tout2 + fout2
# The identity path
n_input_channels = inputs.shape.as_list()[3]
if n_input_channels == n_fmaps:
path2 = inputs
else:
path2 = conv2d(inputs, [1, 1], [1, stride, stride, 1], n_fmaps, FLAGS.w_std, FLAGS.b_init, True, 'SAME', scope_name + '_transform')
# Add and return
assert path1.shape.as_list() == path2.shape.as_list()
out = path1 + path2
out = batch_norm(istrain, out, scope_name + '_addition')
out = tf.nn.relu(out)
return out
