def extract(self, src_file, tgt_file):
'''
Feature extraction
'''
# Read this audio file
y_t = ia.read(src_file,
in_fs=self.config['in_fs'],
out_fs=self.config['work_fs'])[0]
#
# BEAMFORMING
#
# Beamformer poiting at the fromtal direction by default
if len(y_t.shape) > 1 and y_t.shape[1] > 1:
y_t = y_t.sum(1)
#
# SPEECH ENHANCEMENT
#
# Pre-emphasis, STFT
y_t = sip.preemphasis(y_t, coef=self.config['preemcoef'])
Y = sip.stft(y_t, self.config['windowsize'], self.config['shift'],
self.config['nfft'])
# Compute IMCRA
hat_X_LSA = self.se.update(Y)
if (self.config['mmse_method'] == 'MFCC'
or self.config['mmse_method'] == 'Wiener'):
# Get a priori SNR and noise variance
xi, Lambda_D = self.se.get_param(['xi', 'Lambda_D'])
# Get Wiener estimate and residual MSE
G = xi / (1 + xi)
# Use the posterior associated to the Wiener filter
hat_X = G * Y
if self.config['mmse_method'] == 'MFCC':
MSE = G * Lambda_D
else:
MSE = np.zeros(G.shape)
elif self.config['mmse_method'] == 'LSA':
# Use LSA
hat_X = hat_X_LSA
MSE = np.zeros(hat_X.shape)
#
# FEATURE EXTRACTION / UNCERTAINTY PROPAGATION
#
# MFCC
mu_x, Sigma_x = self.mfcc.extract_up(hat_X, MSE)
# CMS
mu_x, Sigma_x = self.mfcc.cms_up(mu_x, Sigma_x)
# Deltas, Accelerations
mu_d, Sigma_d = fe.deltas_up(mu_x, Sigma_x)
mu_a, Sigma_a = fe.deltas_up(mu_d, Sigma_d)
mu_x = np.concatenate((mu_x, mu_d, mu_a))
Sigma_x = np.concatenate((Sigma_x, Sigma_d, Sigma_a))
# Provide uncertainty
if self.config['do_up']:
x = np.concatenate((mu_x, Sigma_x))
else:
x = mu_x
# Plot features, for debug
if self.config['targetformat'] == 'HTK':
htk.writehtkfeats(tgt_file, x, self.config['fp'],
self.config['tc'])
else:
raise ValueError, ("TARGETFORMAT = %s Not supported" %
self.config['targetformat'])
def extract(self, src_file, tgt_file):
'''
Feature extraction
'''
# Read this audio file
y_t = ia.read(src_file, in_fs=self.config['in_fs'],
out_fs=self.config['work_fs'])[0]
#
# BEAMFORMING
#
# Beamformer poiting at the fromtal direction by default
if len(y_t.shape) > 1 and y_t.shape[1] > 1:
y_t = y_t.sum(1)
#
# SPEECH ENHANCEMENT
#
# Pre-emphasis, STFT
y_t = sip.preemphasis(y_t, coef=self.config['preemcoef'])
Y = sip.stft(y_t, self.config['windowsize'], self.config['shift'],
self.config['nfft'])
# Compute IMCRA
hat_X_LSA = self.se.update(Y)
if (self.config['mmse_method'] == 'MFCC' or
self.config['mmse_method'] == 'Wiener'):
# Get a priori SNR and noise variance
xi, Lambda_D = self.se.get_param(['xi', 'Lambda_D'])
# Get Wiener estimate and residual MSE
G = xi/(1+xi)
# Use the posterior associated to the Wiener filter
hat_X = G*Y
if self.config['mmse_method'] == 'MFCC':
MSE = G*Lambda_D
else:
MSE = np.zeros(G.shape)
elif self.config['mmse_method'] == 'LSA':
# Use LSA
hat_X = hat_X_LSA
MSE = np.zeros(hat_X.shape)
#
# FEATURE EXTRACTION / UNCERTAINTY PROPAGATION
#
# MFCC
mu_x, Sigma_x = self.mfcc.extract_up(hat_X, MSE)
# CMS
mu_x, Sigma_x = self.mfcc.cms_up(mu_x, Sigma_x)
# Deltas, Accelerations
mu_d, Sigma_d = fe.deltas_up(mu_x, Sigma_x)
mu_a, Sigma_a = fe.deltas_up(mu_d, Sigma_d)
mu_x = np.concatenate((mu_x, mu_d, mu_a))
Sigma_x = np.concatenate((Sigma_x, Sigma_d, Sigma_a))
# Provide uncertainty
if self.config['do_up']:
x = np.concatenate((mu_x, Sigma_x))
else:
x = mu_x
# Plot features, for debug
if self.config['targetformat'] == 'HTK':
htk.writehtkfeats(tgt_file, x, self.config['fp'], self.config['tc'])
else:
raise ValueError, ("TARGETFORMAT = %s Not supported"
% self.config['targetformat'])
def extract(self, src_file, tgt_file):
'''
Feature extraction
'''
# Get indices for the position of speech and background based on
# external info. If MLF or STM provided for VAD use them.
if 'stm_vad' in self.config:
# VAD SPECIFIED BY A STM
if src_file not in self.config['stm_trans']:
raise EnvironmentError, ("stm file %s has not transcription "
"for %s" %
(self.config['stm_vad'], src_file))
# Collect speech events and preceeding backgrounds
events = []
backgs = []
t_bg = 0
for tr in self.config['stm_trans'][src_file]:
# Preceeding background
if not tr[2]:
backgs.append(None)
else:
backgs.append((0, tr[2] * self.config['in_fs']))
# Speech event
events.append((src_file, tr[2] * self.config['in_fs'],
tr[3] * self.config['in_fs']))
else:
# ONE SINGLE EVENT IN PRESENT MICROPHONE
T = int(self.config['work_fs'] * self.config['init_time'])
events = [(src_file, T, -1)]
backgs = [(0, T)]
# Loop over events in the scene
for backg, event in zip(backgs, events):
# Read this audio file
y_t = ia.read(src_file,
in_fs=self.config['in_fs'],
out_fs=self.config['work_fs'])[0]
#
# BEAMFORMING
#
# Beamformer poiting at the fromtal direction by default
if y_t.shape[1] > 1:
y_t = y_t.sum(1)
#
# SPEECH ENHANCEMENT
#
# Select segment of background preceeding speech
if backg:
d_t = y_t[backg[0]:backg[1]]
else:
d_t = None
# Select segment of speech
y_t = y_t[event[1]:event[2]]
# Pre-emphasis, STFT
y_t = sip.preemphasis(y_t, coef=self.config['preemcoef'])
Y = sip.stft(y_t, self.config['windowsize'], self.config['shift'],
self.config['nfft'])
# Compute IMCRA
self.se.update(Y)
# Get a priori SNR and Wiener gain
xi = self.se.store['xi'][:, :self.se.l]
G = xi / (1 + xi)
# Get Wiener estimate and residual MSE
hat_X_W = G * Y
MSE = G * self.se.store['Lambda_D'][:, :self.se.l]
set_trace()
# MFCC
mu_x, Sigma_x = self.mfcc.extract_up(hat_X_W, MSE)
# CMS
mu_x, Sigma_x = self.mfcc.cms_up(mu_x, Sigma_x)
# Deltas, Accelerations
mu_d, Sigma_d = fe.deltas_up(mu_x, Sigma_x)
mu_a, Sigma_a = fe.deltas_up(mu_d, Sigma_d)
mu_x = np.concatenate((mu_x, mu_d, mu_a))
Sigma_x = np.concatenate((Sigma_x, Sigma_d, Sigma_a))
if self.config['unc_prop']:
x = np.concatenate((mu_x, Sigma_x))
else:
x = mu_x
# Plot features, for debug
htk.writehtkfeats(target_file, x, self.config['fp'],
self.config['tc'])
def extract(self, src_file, tgt_file):
'''
Feature extraction
'''
# Get indices for the position of speech and background based on
# external info. If MLF or STM provided for VAD use them.
if 'stm_vad' in self.config:
# VAD SPECIFIED BY A STM
if src_file not in self.config['stm_trans']:
raise EnvironmentError, ("stm file %s has not transcription "
"for %s" % (self.config['stm_vad'],
src_file))
# Collect speech events and preceeding backgrounds
events = []
backgs = []
t_bg = 0
for tr in self.config['stm_trans'][src_file]:
# Preceeding background
if not tr[2]:
backgs.append(None)
else:
backgs.append((0, tr[2]*self.config['in_fs']))
# Speech event
events.append((src_file, tr[2]*self.config['in_fs'],
tr[3]*self.config['in_fs']))
else:
# ONE SINGLE EVENT IN PRESENT MICROPHONE
T = int(self.config['work_fs']*self.config['init_time'])
events = [(src_file, T, -1)]
backgs = [(0, T)]
# Loop over events in the scene
for backg, event in zip(backgs, events):
# Read this audio file
y_t = ia.read(src_file, in_fs=self.config['in_fs'],
out_fs=self.config['work_fs'])[0]
#
# BEAMFORMING
#
# Beamformer poiting at the fromtal direction by default
if y_t.shape[1] > 1:
y_t = y_t.sum(1)
#
# SPEECH ENHANCEMENT
#
# Select segment of background preceeding speech
if backg:
d_t = y_t[backg[0]:backg[1]]
else:
d_t = None
# Select segment of speech
y_t = y_t[event[1]:event[2]]
# Pre-emphasis, STFT
y_t = sip.preemphasis(y_t, coef=self.config['preemcoef'])
Y = sip.stft(y_t, self.config['windowsize'], self.config['shift'],
self.config['nfft'])
# Compute IMCRA
self.se.update(Y)
# Get a priori SNR and Wiener gain
xi = self.se.store['xi'][:, :self.se.l]
G = xi/(1+xi)
# Get Wiener estimate and residual MSE
hat_X_W = G*Y
MSE = G*self.se.store['Lambda_D'][:, :self.se.l]
set_trace()
# MFCC
mu_x, Sigma_x = self.mfcc.extract_up(hat_X_W, MSE)
# CMS
mu_x, Sigma_x = self.mfcc.cms_up(mu_x, Sigma_x)
# Deltas, Accelerations
mu_d, Sigma_d = fe.deltas_up(mu_x, Sigma_x)
mu_a, Sigma_a = fe.deltas_up(mu_d, Sigma_d)
mu_x = np.concatenate((mu_x, mu_d, mu_a))
Sigma_x = np.concatenate((Sigma_x, Sigma_d, Sigma_a))
if self.config['unc_prop']:
x = np.concatenate((mu_x, Sigma_x))
else:
x = mu_x
# Plot features, for debug
htk.writehtkfeats(target_file, x, self.config['fp'], self.config['tc'])