audioflux.cqcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, cqt_num=84, samplate=32000, low_fre=32.70319566257483, slide_length=None, bin_per_octave=12, window_type=WindowType.HANN, normal_type=SpectralFilterBankNormalType.AREA, is_scale=True)

Constant-Q cepstral coefficients (CQCCs)


We recommend using the CQT class, you can use it more flexibly and efficiently.

X: np.ndarray [shape=(…, n)]

audio time series.

cc_num: int

number of GTCC to return.

rectify_type: CepstralRectifyType

cepstral rectify type

cqt_num: int

Number of cqt frequency bins to generate, starting at low_fre.

samplate: int

Sampling rate of the incoming audio.

low_fre: float or None

Lowest frequency.

slide_length: int or None

Window sliding length.

If slide_length is None, then slide_length = fft_length / 4

bin_per_octave: int

Number of bins per octave.

window_type: WindowType

Window type for each frame.

See: type.WindowType

normal_type: SpectralFilterBankNormalType

Spectral filter normal type. It determines the type of normalization.

See: type.SpectralFilterBankNormalType

is_scale: bool

Whether to use scale.

out: np.ndarray [shape=(…, cc_num, time)]

The matrix of CQCCs

fre_band_arr: np:ndarray [shape=(fre,)]

The array of cqt frequency bands

See also



Read 220Hz audio data

>>> import audioflux as af
>>> audio_path = af.utils.sample_path('220')
>>> audio_arr, sr =

Extract cqcc data

>>> cc_arr, _ = af.cqcc(audio_arr, samplate=sr)

Show plot

>>> import matplotlib.pyplot as plt
>>> from audioflux.display import fill_spec
>>> import numpy as np
>>> # calculate x-coords
>>> audio_len = audio_arr.shape[-1]
>>> x_coords = np.linspace(0, audio_len/sr, cc_arr.shape[-1] + 1)
>>> fig, ax = plt.subplots()
>>> img = fill_spec(cc_arr, axes=ax,
>>>                 x_coords=x_coords, x_axis='time',
>>>                 title='CQCC')
>>> fig.colorbar(img, ax=ax)