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spafe.features.bfcc#

Description : Bark Frequency Cepstral Coefﬁcients (BFCCs) extraction algorithm implementation.
Copyright (c) 2019-2023 Ayoub Malek. This source code is licensed under the terms of the BSD 3-Clause License. For a copy, see <https://github.com/SuperKogito/spafe/blob/master/LICENSE>.

spafe.features.bfcc.intensity_power_law(w: ndarray) → ndarray[source]#

Apply the intensity power law based on [Hermansky] .

Parameters: w (numpy.ndarray) – signal information.
Returns: array after intensity power law.
Return type: (numpy.ndarray)

Note

\[E(\omega) = \frac{(\omega^{2}+56.8 \times 10^{6}) \omega^{4}}{(\omega^{2}+6.3 \times 10^{6})^{2} \times (\omega^{2}+0.38 \times 10^{9})}\]

spafe.features.bfcc.bark_spectrogram(sig: ndarray, fs: int = 16000, pre_emph: float = 0, pre_emph_coeff: float = 0.97, window: Optional[SlidingWindow] = None, nfilts: int = 24, nfft: int = 512, low_freq: float = 0, high_freq: Optional[float] = None, scale: Literal['ascendant', 'descendant', 'constant'] = 'constant', fbanks: Optional[ndarray] = None, conversion_approach: Literal['Wang', 'Tjomov', 'Schroeder', 'Terhardt', 'Zwicker', 'Traunmueller'] = 'Wang')[source]#

Compute the bark scale spectrogram.

Parameters

sig (numpy.ndarray) – 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. (Default is 13).
pre_emph (bool) – apply pre-emphasis if 1. (Default is True).
pre_emph_coeff (float) – pre-emphasis filter coefficient). (Default is 0.97).
window (SlidingWindow) – sliding window object. (Default is None).
nfilts (int) – the number of filters in the filter bank. (Default is 40).
nfft (int) – number of FFT points. (Default is 512).
low_freq (float) – lowest band edge of mel filters (Hz). (Default is 0).
high_freq (float) – highest band edge of mel filters (Hz). (Default is samplerate/2).
scale (str) – monotonicity behavior of the filter banks. (Default is “constant”).
fbanks (numpy.ndarray) – filter bank matrix. (Default is None).
conversion_approach (str) – bark scale conversion approach. (Default is “Wang”).

Returns

(numpy.ndarray) : spectrogram matrix.
(numpy.ndarray) : fourrier transform.

Return type

(tuple)

Tip

scale : can take the following options [“constant”, “ascendant”, “descendant”].
conversion_approach : can take the following options [“Tjomov”,”Schroeder”, “Terhardt”, “Zwicker”, “Traunmueller”, “Wang”]. Note that the use of different options than the ddefault can lead to unexpected behavior/issues.

Note

../_images/bark_spectrogram.png — Architecture of Bark spectrogram computation algorithm.#

Examples

from spafe.features.bfcc import bark_spectrogram
from spafe.utils.vis import show_spectrogram
from spafe.utils.preprocessing import SlidingWindow
from scipy.io.wavfile import read

# read audio
fpath = "../../../tests/data/test.wav"
fs, sig = read(fpath)

# compute bark spectrogram
bSpec, bfreqs = bark_spectrogram(sig,
                                fs=fs,
                                pre_emph=0,
                                pre_emph_coeff=0.97,
                                window=SlidingWindow(0.03, 0.015, "hamming"),
                                nfilts=128,
                                nfft=2048,
                                low_freq=0,
                                high_freq=fs/2)

# visualize spectrogram
show_spectrogram(bSpec.T,
                 fs=fs,
                 xmin=0,
                 xmax=len(sig)/fs,
                 ymin=0,
                 ymax=(fs/2)/1000,
                 dbf=80.0,
                 xlabel="Time (s)",
                 ylabel="Frequency (kHz)",
                 title="Bark spectrogram (dB)",
                 cmap="jet")

spafe.features.bfcc.bfcc(sig: ndarray, fs: int = 16000, num_ceps: int = 13, pre_emph: bool = True, pre_emph_coeff: float = 0.97, window: Optional[SlidingWindow] = None, nfilts: int = 26, nfft: int = 512, low_freq: float = 0, high_freq: Optional[float] = None, scale: Literal['ascendant', 'descendant', 'constant'] = 'constant', dct_type: int = 2, use_energy: bool = False, lifter: Optional[int] = None, normalize: Optional[Literal['mvn', 'ms', 'vn', 'mn']] = None, fbanks: Optional[ndarray] = None, conversion_approach: Literal['Wang', 'Tjomov', 'Schroeder', 'Terhardt', 'Zwicker', 'Traunmueller'] = 'Wang') → ndarray[source]#

Compute the Bark Frequency Cepstral Coefﬁcients (BFCCs) from an audio signal as described in [Kaminska].

Parameters

sig (numpy.ndarray) – 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. (Default is 13).
pre_emph (bool) – apply pre-emphasis if 1. (Default is True).
pre_emph_coeff (float) – pre-emphasis filter coefﬁcient. (Default is 0.97).
window (SlidingWindow) – sliding window object. (Default is None).
nfilts (int) – the number of filters in the filter bank. (Default is 40).
nfft (int) – number of FFT points. (Default is 512).
low_freq (float) – lowest band edge of mel filters (Hz). (Default is 0).
high_freq (float) – highest band edge of mel filters (Hz). (Default is samplerate/2).
scale (str) – monotonicity behavior of the filter banks. (Default is “constant”).
dct_type (int) – type of DCT used. (Default is 2).
use_energy (int) – overwrite C0 with true log energy. (Default is 0).
lifter (int) – apply liftering if specified. (Default is None).
normalize (str) – apply normalization if approach specified. (Default is None).
fbanks (numpy.ndarray) – filter bank matrix. (Default is None).
conversion_approach (str) – bark scale conversion approach. (Default is “Wang”).

Returns

2d array of BFCC features (num_frames x num_ceps).

Return type

(numpy.ndarray)

Raises

ParameterError – if nfilts < num_ceps

Tip

dct : can take the following options [1, 2, 3, 4].
normalize : can take the following options [“mvn”, “ms”, “vn”, “mn”].
conversion_approach : can take the following options [“Tjomov”,”Schroeder”, “Terhardt”, “Zwicker”, “Traunmueller”, “Wang”]. Note that the use of different options than the ddefault can lead to unexpected behavior/issues.

Note

../_images/bfccs.png — Architecture of Bark frequency cepstral coefﬁcients extraction algorithm.#

References

Kaminska: : Kamińska, D. & Sapiński, T. & Anbarjafari, G. (2017). Efficiency of chosen speech descriptors in relation to emotion recognition. EURASIP Journal on Audio Speech and Music Processing. 2017. 10.1186/s13636-017-0100-x.

Examples

from scipy.io.wavfile import read
from spafe.features.bfcc import bfcc
from spafe.utils.preprocessing import SlidingWindow
from spafe.utils.vis import show_features

# read audio
fpath = "../../../tests/data/test.wav"
fs, sig = read(fpath)

# compute bfccs
bfccs  = bfcc(sig,
              fs=fs,
              pre_emph=1,
              pre_emph_coeff=0.97,
              window=SlidingWindow(0.03, 0.015, "hamming"),
              nfilts=128,
              nfft=2048,
              low_freq=0,
              high_freq=8000,
              normalize="mvn")

# visualize features
show_features(bfccs, "Bark Frequency Cepstral Coefﬁcients", "BFCC Index", "Frame Index")

spafe.features

spafe.features.cqcc