audioflux.bfcc

audioflux.bfcc(X, cc_num=13, rectify_type=CepstralRectifyType.LOG, bark_num=128, radix2_exp=12, samplate=32000, slide_length=None, low_fre=None, high_fre=None, window_type=WindowType.HANN)

Bark-frequency cepstral coefficients (BFCCs)

Note

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

Parameters
X: np.ndarray [shape=(…, n)]

audio time series.

cc_num: int

number of BFCC to return.

rectify_type: CepstralRectifyType

cepstral rectify type

bark_num: int

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

radix2_exp: int

fft_length=2**radix2_exp

samplate: int

Sampling rate of the incoming audio.

slide_length: int or None

Window sliding length.

If slide_length is None, then slide_length = fft_length / 4

low_fre: float or None

Lowest frequency.

high_fre: float or None

Highest frequency. Default is 16000(samplate/2).

window_type: WindowType

Window type for each frame.

See: type.WindowType

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

The matrix of BFCCs

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

The array of Bark frequency bands

See also

mfcc
gtcc
cqcc
BFT
XXCC

Examples

Read 220Hz audio data

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

Extract bfcc data

>>> cc_arr, _ = af.bfcc(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='BFCC')
>>> fig.colorbar(img, ax=ax)
../_images/audioflux-bfcc-1.png