def test_lpcc(sig, fs, num_ceps, lifter, normalize): """ test LPCC features module for the following: - check that the returned number of cepstrums is correct. - check normalization. - check liftering. """ lpccs = lpcc(sig=sig, fs=fs, num_ceps=num_ceps, lifter=lifter, normalize=normalize) # assert number of returned cepstrum coefficients if not lpccs.shape[1] == num_ceps: raise AssertionError # TO FIX: normalize if normalize: np.testing.assert_almost_equal( lpccs, cmvn( cms( lpcc(sig=sig, fs=fs, num_ceps=num_ceps, lifter=lifter, normalize=False))), 0) else: # TO FIX: lifter if lifter > 0: np.testing.assert_almost_equal( lpccs, lifter_ceps( lpcc(sig=sig, fs=fs, num_ceps=num_ceps, lifter=False, normalize=normalize), lifter), 0)
def test_pncc(sig, fs, num_ceps, nfilts, nfft, low_freq, high_freq, dct_type, use_energy, lifter, normalize): """ test PNCC features module for the following: - check if ParameterErrors are raised for: - nfilts < num_ceps - negative low_freq value - high_freq > fs / 2 - check that the returned number of cepstrums is correct. - check the use energy functionality. - check normalization. - check liftering. """ # check error for number of filters is smaller than number of cepstrums with pytest.raises(ParameterError): pnccs = pncc(sig=sig, fs=fs, num_ceps=num_ceps, nfilts=num_ceps - 1, nfft=nfft, low_freq=low_freq, high_freq=high_freq) # check lifter Parameter error for low freq with pytest.raises(ParameterError): pnccs = pncc(sig=sig, fs=fs, num_ceps=num_ceps, nfilts=nfilts, nfft=nfft, low_freq=-5, high_freq=high_freq) # check lifter Parameter error for high freq with pytest.raises(ParameterError): pnccs = pncc(sig=sig, fs=fs, num_ceps=num_ceps, nfilts=nfilts, nfft=nfft, low_freq=low_freq, high_freq=16000) # compute features pnccs = pncc(sig=sig, fs=fs, num_ceps=num_ceps, nfilts=nfilts, nfft=nfft, low_freq=low_freq, high_freq=high_freq, dct_type=dct_type, use_energy=use_energy, lifter=lifter, normalize=normalize) # assert number of returned cepstrum coefficients if not pnccs.shape[1] == num_ceps: raise AssertionError # check use energy if use_energy: pnccs_energy = pnccs[:, 0] gfccs_energy = pncc(sig=sig, fs=fs, num_ceps=num_ceps, nfilts=nfilts, nfft=nfft, low_freq=low_freq, high_freq=high_freq, dct_type=dct_type, use_energy=use_energy, lifter=lifter, normalize=normalize)[:, 0] np.testing.assert_almost_equal(pnccs_energy, gfccs_energy, 3) # check normalize if normalize: np.testing.assert_almost_equal( pnccs, cmvn( cms( pncc(sig=sig, fs=fs, num_ceps=num_ceps, nfilts=nfilts, nfft=nfft, low_freq=low_freq, high_freq=high_freq, dct_type=dct_type, use_energy=use_energy, lifter=lifter, normalize=False))), 3) else: # check lifter if lifter > 0: np.testing.assert_almost_equal( pnccs, lifter_ceps( pncc(sig=sig, fs=fs, num_ceps=num_ceps, nfilts=nfilts, nfft=nfft, low_freq=low_freq, high_freq=high_freq, dct_type=dct_type, use_energy=use_energy, lifter=False, normalize=normalize), lifter), 3)
def test_cmvn(x): # To improve x = np.round(np.random.normal(loc=0.0, scale=1.0, size=100), 3) y = cmvn(x) np.testing.assert_almost_equal(y, cvn(cms(x)), 0)
def test_cms(x): x = np.round(np.random.normal(loc=0.0, scale=1.0, size=100), 3) y = cms(x) np.testing.assert_almost_equal(y, x, 0)
def lpcc(sig, fs=16000, num_ceps=13, pre_emph=1, pre_emph_coeff=0.97, win_type="hann", win_len=0.025, win_hop=0.01, do_rasta=True, lifter=1, normalize=1, dither=1): """ Compute the LINEAR PREDICTIVE CEPSTRAL COEFFICIENTS (LPCC) from an audio signal. Args: sig (array) : a mono audio signal (Nx1) from which to compute features. fs (int) : the sampling frequency of the signal we are working with. Default is 16000. num_ceps (float) : number of cepstra to return (order of the model to compute). Default is 13. pre_emph (int) : apply pre-emphasis if 1. Default is 1. pre_emph_coeff (float) : apply pre-emphasis filter [1 -pre_emph] (0 = none). Default is 0.97. win_type (float) : window type to apply for the windowing. Default is hanning. win_len (float) : window length in sec. Default is 0.025. win_hop (float) : step between successive windows in sec. Default is 0.01. do_rasta (int) : if 1 then apply rasta filtering. Default is 0. lifter (int) : apply liftering if value > 0. Default is 22. normalize (int) : apply normalization if 1. Default is 0. dither (int) : 1 = add offset to spectrum as if dither noise. Default is 0. Returns: (array) : 2d array of LPCC features (num_frames x num_ceps) """ lpcs = lpc(sig=sig, fs=fs, num_ceps=num_ceps, pre_emph=pre_emph, pre_emph_coeff=pre_emph_coeff, win_len=win_len, win_hop=win_hop, do_rasta=True, dither=dither) lpccs = lpc2cep(lpcs.T) # liftering if lifter > 0: lpccs = lifter_ceps(lpccs, lifter) # normalization if normalize: lpccs = cmvn(cms(lpccs)) lpccs = lpccs.T return lpccs[:, :]