def log_cepstrum(data, fs, nc, p, n, w): # @UnusedVariable: n is never used!!!
"""
Cepstrum of a signal.
Computes the cepstral coefficient on a logarithmic scale of the given data
which can be windowed or not.
If data are windowed the analytic signal and the envelope of each window is
returned.
:type data: :class:`~numpy.ndarray`
:param data: Data to make envelope of.
:param fs: Sampling frequency in Hz.
:param nc: number of cepstral coefficients.
:param p: Number of filters in filterbank.
:param n: Number of data windows.
:return: Cepstral coefficients.
"""
new_dtype = np.float32 if data.dtype.itemsize == 4 else np.float64
data = np.require(data, dtype=new_dtype)
dataT = np.transpose(data)
nfft = util.next_pow_2(dataT.shape[0])
fc = fftpack.fft(dataT, nfft, 0)
f = fc[1:len(fc) // 2 + 1, :]
m, a, b = log_spaced_filterbank_matrix(p, nfft, fs, w)
pw = np.real(np.multiply(f[a:b, :], np.conj(f[a:b, :])))
pth = np.max(pw) * 1E-20
ath = np.sqrt(pth)
# h1 = np.transpose(np.array([[ath] * int(b + 1 - a)]))
# h2 = m * abs(f[a - 1:b, :])
y = np.log(np.maximum(m * abs(f[a - 1:b, :]), ath))
z = util.rdct(y)
z = z[1:, :]
# nc = nc + 1
nf = np.size(z, 1)
if (p > nc):
z = z[:nc, :]
elif (p < nc):
z = np.vstack([z, np.zeros(nf, nc - p)])
return z
def log_cepstrum(data, fs, nc, p, n, w): # @UnusedVariable: n is never used!!!
"""
Cepstrum of a signal.
Computes the cepstral coefficient on a logarithmic scale of the given data
which can be windowed or not.
If data are windowed the analytic signal and the envelope of each window is
returned.
:type data: :class:`~numpy.ndarray`
:param data: Data to make envelope of.
:param fs: Sampling frequency in Hz.
:param nc: number of cepstral coefficients.
:param p: Number of filters in filterbank.
:param n: Number of data windows.
:return: Cepstral coefficients.
"""
new_dtype = np.float32 if data.dtype.itemsize == 4 else np.float64
data = np.require(data, dtype=new_dtype)
data_t = np.transpose(data)
nfft = util.next_pow_2(data_t.shape[0])
fc = fftpack.fft(data_t, nfft, 0)
f = fc[1:len(fc) // 2 + 1, :]
m, a, b = log_spaced_filterbank_matrix(p, nfft, fs, w)
pw = np.real(np.multiply(f[a:b, :], np.conj(f[a:b, :])))
pth = np.max(pw) * 1E-20
ath = np.sqrt(pth)
# h1 = np.transpose(np.array([[ath] * int(b + 1 - a)]))
# h2 = m * abs(f[a - 1:b, :])
y = np.log(np.maximum(m * abs(f[a - 1:b, :]), ath))
z = util.rdct(y)
z = z[1:, :]
# nc = nc + 1
nf = np.size(z, 1)
if (p > nc):
z = z[:nc, :]
elif (p < nc):
z = np.vstack([z, np.zeros(nf, nc - p)])
return z
