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spafe.features.lfcc#

Description : Linear Frequency Cepstral Coefﬁcients (LFCCs) 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.lfcc.linear_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) → ndarray[source]#

Compute the mel 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).
pre_emph (bool) – apply pre-emphasis if 1. (Default is 1).
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).

Returns

features - the LFFC features: num_frames x num_ceps

Return type

(numpy.ndarray)

Tip

scale : can take the following options [“constant”, “ascendant”, “descendant”].

Note

../_images/linear_spectrogram.png — Architecture of linear scale spectrogram extraction algorithm.#

Examples

from spafe.features.lfcc import linear_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 spectrogram
lSpec, lfreqs = linear_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(lSpec.T,
                 fs,
                 xmin=0,
                 xmax=len(sig)/fs,
                 ymin=0,
                 ymax=(fs/2)/1000,
                 dbf=80.0,
                 xlabel="Time (s)",
                 ylabel="Frequency (kHz)",
                 title="Linear spectrogram (dB)",
                 cmap="jet")

spafe.features.lfcc.lfcc(sig, fs: int = 16000, num_ceps=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=2, use_energy=False, lifter: Optional[int] = None, normalize: Optional[Literal['mvn', 'ms', 'vn', 'mn']] = None, fbanks: Optional[ndarray] = None)[source]#

Compute the linear-frequency cepstral coefﬁcients (GFCC features) from an audio signal as described in [Sehili].

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 1).
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 (bool) – overwrite C0 with true log energy. (Default is 0).
lifter (int) – apply liftering if specified. (Default is None).
normalize (str) – apply normalization if approach provided. (Default is None).
fbanks (numpy.ndarray) – filter bank matrix. (Default is None).

Returns

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

Return type

(numpy.ndarray)

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”].

Note

../_images/lfccs.png — Architecture of linear frequency cepstral coefﬁcients extraction algorithm.#

Examples

from scipy.io.wavfile import read
from spafe.features.lfcc import lfcc
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 lfccs
lfccs  = lfcc(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(lfccs, "Linear Frequency Cepstral Coefﬁcients", "LFCC Index", "Frame Index")

References

Sehili: : Sehili, A. & Istrate, B. & Boudy, J. (2022). Primary Investigation of Sound Recognition for a domotic application using Support Vector.

spafe.features.gfcc

spafe.features.lpc