def plots(m_mag, m_real, m_imag, v_f0):
lp.plotm(la.db(m_mag)) # in decibels for better visualisation
lp.title('Magnitude Spectrum (dB)')
lp.xlabel('Time (frames)')
lp.ylabel('Frequency bins')
lp.plotm(m_real)
lp.title('"R" Feature Phase Spectrum')
lp.xlabel('Time (frames)')
lp.ylabel('Frequency bins')
lp.plotm(m_imag)
lp.title('"I" Feature Phase Spectrum')
lp.xlabel('Time (frames)')
lp.ylabel('Frequency bins')
lp.figure()
lp.plot(v_f0)
lp.title('F0')
lp.xlabel('Time (frames)')
lp.ylabel('F0')
lp.grid()
return
def plots(m_mag, m_real, m_imag, v_f0):
lp.plotm(la.db(m_mag)) # in decibels for better visualisation
lp.title('Magnitude Spectrum (dB)')
lp.xlabel('Time (frames)')
lp.ylabel('Frequency bins')
lp.plotm(m_real)
lp.title('"R" Feature Phase Spectrum')
lp.xlabel('Time (frames)')
lp.ylabel('Frequency bins')
lp.plotm(m_imag)
lp.title('"I" Feature Phase Spectrum')
lp.xlabel('Time (frames)')
lp.ylabel('Frequency bins')
lp.figure()
lp.plot(v_f0)
lp.title('F0')
lp.xlabel('Time (frames)')
lp.ylabel('F0')
lp.grid()
return
def plots(m_mag_mel_log, m_real_mel, m_imag_mel, v_lf0):
lp.plotm(m_mag_mel_log)
lp.title('Log-Magnitude Spectrum')
lp.xlabel('Time (frames)')
lp.ylabel('Mel-scaled frequency bins')
lp.plotm(m_real_mel)
lp.title('"R" Feature Phase Spectrum')
lp.xlabel('Time (frames)')
lp.ylabel('Mel-scaled frequency bins')
lp.plotm(m_imag_mel)
lp.title('"I" Feature Phase Spectrum')
lp.xlabel('Time (frames)')
lp.ylabel('Mel-scaled frequency bins')
lp.figure()
lp.plot(np.exp(v_lf0)) # unlog for better visualisation
lp.title('F0')
lp.xlabel('Time (frames)')
lp.ylabel('F0')
lp.grid()
return
def true_envelope(m_sp, in_type='abs', ncoeffs=60, thres_db=0.1):
'''
in_type: 'abs', 'db', or 'log'
TODO: Test cases 'db' and 'log'
'''
if in_type == 'db':
m_sp_db = m_sp
elif in_type == 'abs':
m_sp_db = db(m_sp)
elif in_type == 'log':
m_sp_db = (20.0 / np.log(10.0)) * m_sp
m_sp_db_env = np.zeros(m_sp_db.shape)
nFrms = m_sp_db.shape[0]
n_maxiter = 100
for f in xrange(nFrms):
v_sp_db = m_sp_db[f, :]
for i in xrange(n_maxiter):
v_sp_db_sm = spectral_smoothing_rceps(v_sp_db[None, :],
nc_total=ncoeffs,
fade_to_total=0.7)[0]
# Debug:
if False:
from libplot import lp
lp.figure(1)
lp.plot(m_sp_db[f, :], '.-b')
lp.plot(v_sp_db, '.-r')
lp.plot(v_sp_db_sm, '.-g')
lp.grid()
if np.mean(np.abs(v_sp_db - v_sp_db_sm)) < thres_db:
break
v_sp_db = np.maximum(v_sp_db, v_sp_db_sm)
m_sp_db_env[f, :] = v_sp_db_sm
if in_type == 'db':
m_sp_env = m_sp_db_env
elif in_type == 'abs':
m_sp_env = db(m_sp_db_env, b_inv=True)
elif in_type == 'log':
m_sp_env = (np.log(10.0) / 20.0) * m_sp_db_env
return m_sp_env
alpha=alpha,
in_type='log')))
if False:
lp.plotm(m_mag_ref)
lp.plotm(m_mag_a)
lp.plotm(m_mag_ref - m_mag_a)
lp.plotm((m_mag_ref - m_mag_a)[:50, :50])
lp.plotm(np.diff(m_mag_ref, axis=0))
lp.plotm(np.diff(m_mag_a, axis=0))
if True:
nx = 26
lp.figure()
lp.plot(m_mag_ref[nx, :])
lp.plot(m_mag_a[nx, :])
lp.grid()
if True:
nx = 26
lp.figure()
lp.plot(m_mag_ref[nx, :])
lp.plot(m_mag_a[nx, :])
lp.plot(m_mag_ref[nx - 1, :])
lp.plot(m_mag_a[nx - 1, :])
lp.grid()
nx = 37
lp.figure()
lp.plot(m_mag_ref[nx, :])
lp.plot(m_mag_a[nx, :])