def arspec(x, order, nfft=None, fs=1):
"""Compute the spectral density using an AR model.
An AR model of the signal is estimated through the Yule-Walker equations;
the estimated AR coefficient are then used to compute the spectrum, which
can be computed explicitely for AR models.
Parameters
----------
x : array-like
input signal
order : int
Order of the LPC computation.
nfft : int
size of the fft to compute the periodogram. If None (default), the
length of the signal is used. if nfft > n, the signal is 0 padded.
fs : float
Sampling rate. By default, is 1 (normalized frequency. e.g. 0.5 is the
Nyquist limit).
Returns
-------
pxx : array-like
The psd estimate.
fgrid : array-like
Frequency grid over which the periodogram was estimated.
"""
x = np.atleast_1d(x)
n = x.size
if x.ndim > 1:
raise ValueError("Only rank 1 input supported for now.")
if not np.isrealobj(x):
raise ValueError("Only real input supported for now.")
if not nfft:
nfft = n
if nfft < n:
raise ValueError("nfft < signal size not supported yet")
a, e, k = lpc(x, order)
# This is not enough to deal correctly with even/odd size
if nfft % 2 == 0:
pn = nfft / 2 + 1
else:
pn = (nfft + 1) / 2
px = 1 / np.fft.fft(a, nfft)[:pn]
pxx = np.real(np.conj(px) * px)
pxx /= fs / e
fx = np.linspace(0, fs * 0.5, pxx.size)
return pxx, fx
def arspec(x, order, nfft=None, fs=1):
"""Compute the spectral density using an AR model.
An AR model of the signal is estimated through the Yule-Walker equations;
the estimated AR coefficient are then used to compute the spectrum, which
can be computed explicitely for AR models.
Parameters
----------
x : array-like
input signal
order : int
Order of the LPC computation.
nfft : int
size of the fft to compute the periodogram. If None (default), the
length of the signal is used. if nfft > n, the signal is 0 padded.
fs : float
Sampling rate. By default, is 1 (normalized frequency. e.g. 0.5 is the
Nyquist limit).
Returns
-------
pxx : array-like
The psd estimate.
fgrid : array-like
Frequency grid over which the periodogram was estimated.
"""
x = np.atleast_1d(x)
n = x.size
if x.ndim > 1:
raise ValueError("Only rank 1 input supported for now.")
if not np.isrealobj(x):
raise ValueError("Only real input supported for now.")
if not nfft:
nfft = n
if nfft < n:
raise ValueError("nfft < signal size not supported yet")
a, e, k = lpc(x, order)
# This is not enough to deal correctly with even/odd size
if nfft % 2 == 0:
pn = nfft / 2 + 1
else:
pn = (nfft + 1 )/ 2
px = 1 / np.fft.fft(a, nfft)[:pn]
pxx = np.real(np.conj(px) * px)
pxx /= fs / e
fx = np.linspace(0, fs * 0.5, pxx.size)
return pxx, fx
def encode(fn, codebook_lpc, codebook_e):
out = zeros(len(data) * 2)
out = []
mean_sig = 0
pitches = []
#signal [0] je fs
signal = scipy.io.wavfile.read(fn)[1] / 2.0**15
for idx, frame in enumerate(make_frames(
signal)): #sf.frames(fn, framesize=FRAME_SIZE, overlap=OVERLAP)):
#mean_sig=0.1*mean(frame)+0.9*mean_sig
frame -= mean(signal)
#autokorelacni koeficienty
c = (correlate(frame, frame, mode='full'))
c = c[len(c) / 2:]
f0_sam = get_f0(c)
pitches.append(f0_sam)
is_voiced = voiced(c)
if not is_voiced:
f0_sam = 0
#frame = preemphasis(frame)
#rxx = correlate(frame,frame, mode='full')
#rxx = rxx[len(rxx) / 2:]
#a, k = levinson_algorithm(rxx[:LPC + 1])
#e = sqrt(mean(lfilter(a, (1,), frame)**2))
a, e, _ = lpc(frame, LPC)
e = sqrt(e)[0]
# vezmeme z codebooku
e_idx = vq(e, codebook_e)[0]
a = a.reshape((1, LPC + 1))
a_idx = vq(a, codebook_lpc)[0]
# e = rms(lfilter(a, (1,), frame))
out.append(a_idx)
out.append(e_idx)
out.append(f0_sam)
#fig, axs = plt.subplots(1, 1)
#axs.plot(frame)
return numpy.asarray(out)
def atal(x, order, num_coefs):
x = np.atleast_1d(x)
n = x.size
if x.ndim > 1:
raise ValueError("Only rank 1 input supported for now.")
if not np.isrealobj(x):
raise ValueError("Only real input supported for now.")
a, e, kk = lpc(x, order)
c = np.zeros(num_coefs)
c[0] = a[0]
for m in range(1, order + 1):
c[m] = -a[m]
for k in range(1, m):
c[m] += (float(k) / float(m) - 1) * a[k] * c[m - k]
for m in range(order + 1, num_coefs):
for k in range(1, order + 1):
c[m] += (float(k) / float(m) - 1) * a[k] * c[m - k]
return c
def get_lpc(wav_dir):
sample_rate, audio = scipy.io.wavfile.read(wav_dir)
audio_array = numpy.array(audio)
lpcs_a, lpcs_e, lpcs_k = lpc(audio_array, 12)
return list(lpcs_a)
files.close()
num = 0
posi_data = numpy.ones((1, 1))
for audio_file in file_list:
(rate, audio_ori) = wav.read(audio_file.rstrip(".raw\n")+".wav")
with open(audio_file.rstrip("nn.raw\n")+".wrd") as files:
seg_info = files.readlines()
files.close()
for info in seg_info:
[start, end] = info.split()[0:2]
#shave head and tail
audio = audio_ori[int(start) + int(window_length * rate):int(end) - int(window_length * rate)]
for idx in range(0, audio.shape[0] - int(window_length * rate), int(voca_step * rate)):
chip = audio[idx:idx + int(window_length * rate)]
(l, _, _) = lpc(chip, lpc_order)
m = mfcc.calcMFCC_delta_delta(signal=chip, samplerate=rate, win_length=window_length)[0]
cnt_data = numpy.hstack((l, m))
if posi_data.size == 1:
posi_data = cnt_data
else:
posi_data = numpy.row_stack((posi_data, cnt_data))
if posi_data.shape[0] > max_length:
numpy.save("posi/data"+str(num), posi_data)
print "posi/data"+str(num)
print audio_file
del posi_data
num = num + 1
posi_data = numpy.ones((1,1))
numpy.save("posi/data"+str(num), posi_data)
