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spafe.features.gfcc#

Description : Gammatone Frequency Cepstral Coefﬁcients (GFCCs) 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.gfcc.erb_spectrogram(sig: ndarray, fs: int = 16000, pre_emph: bool = True, 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['Glasberg'] = 'Glasberg') → Tuple[ndarray, ndarray][source]#

Compute the Gammatone/ erb scale spectrogram also known as Cochleagram.

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).
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) – approach to use for conversion to the erb scale. (Default is “Glasberg”).

Returns

(numpy.ndarray) : the erb spectrogram (num_frames x nfilts)
(numpy.ndarray) : the fourrier transform matrix.

Return type

(tuple)

Tip

scale : can take the following options [“constant”, “ascendant”, “descendant”].
conversion_approach : can take the following options [“Glasberg”]. Note that the use of different options than the default can lead to unexpected behavior/issues.

Note

../_images/gammatone_spectrogram.png — Architecture of the Gammatone spectrogram computation algorithm.#

Examples

from spafe.features.gfcc import erb_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 erb spectrogram
gSpec, gfreqs = erb_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(gSpec.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="Erb spectrogram (dB)",
                 cmap="jet")

spafe.features.gfcc.gfcc(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 = 24, 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['Glasberg'] = 'Glasberg') → ndarray[source]#

Compute the Gammatone-Frequency Cepstral Coefﬁcients (GFCC features) from an audio signal as described in [Jeevan] and [Xu].

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 (int) – 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 value given. (Default is None).
normalize (str) – apply normalization if type specified. (Default is None).
fbanks (numpy.ndarray) – filter bank matrix. (Default is None).
conversion_approach (str) – erb scale conversion approach. (Default is “Glasberg”).

Returns

2d array of GFCC features (num_frames x num_ceps)

Return type

(numpy.ndarray)

Raises

ParameterError – if nfilts < num_ceps

Tip

scale : can take the following options [“constant”, “ascendant”, “descendant”].
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 [“Glasberg”]. Note that the use of different options than the default can lead to unexpected behavior/issues.

Note

../_images/gfccs.png — Architecture of the Gammatone frequency cepstral coefﬁcients extraction algorithm.#

References

Jeevan: : Jeevan, M., Dhingra, A., Hanmandlu, M., & Panigrahi, B. K. (2016). Robust Speaker Verification Using GFCC Based i-Vectors. Proceedings of the International Conference on Signal, Networks, Computing, and Systems, 85–91. doi:10.1007/978-81-322-3592-7_9
Xu: : Xu, H., Lin, L., Sun, X., & Jin, H. (2012). A New Algorithm for Auditory Feature Extraction. 2012 International Conference on Communication Systems and Network Technologies. doi:10.1109/csnt.2012.57

Examples

from scipy.io.wavfile import read
from spafe.features.gfcc import gfcc
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 mfccs and mfes
gfccs  = gfcc(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(gfccs, "Gammatone Frequency Cepstral Coefﬁcients", "GFCC Index", "Frame Index")

spafe.features.cqcc

spafe.features.lfcc