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spafe.features.ngcc#

Description : Normalized Gammachirp Cepstral Coefficients (NGCCs) extraction algorithm implementation.
Copyright (c) 2019-2024 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.ngcc.ngcc(sig: ndarray, 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: Optional[float] = 0, high_freq: Optional[float] = None, scale: typing_extensions.Literal[ascendant, descendant, constant] = 'constant', dct_type: int = 2, use_energy: bool = False, lifter: Optional[int] = None, normalize: Optional[typing_extensions.Literal[mvn, ms, vn, mn]] = None, fbanks: Optional[ndarray] = None, conversion_approach: typing_extensions.Literal[Glasberg] = 'Glasberg') → ndarray[source]#

Compute the normalized gammachirp cepstral coefﬁcients (NGCC features) from an audio signal according to [Zouhir].

Parameters

sig (numpy.ndarray) – input mono audio signal (Nx1).
fs (int) – signal sampling frequency. (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 (bool) – overwrite C0 with true log energy (Default is False).
lifter (int) – apply liftering if specified. (Default is None).
normalize (str) – apply normalization if 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 NGCC 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”].
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/ngccs.png — Architecture of normalized gammachirp cepstral coefﬁcients extraction algorithm.#

Examples

from scipy.io.wavfile import read
from spafe.features.ngcc import ngcc
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 ngccs
ngccs  = ngcc(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(ngccs, "Normalized Gammachirp Cepstral Coefficients", "NGCC Index", "Frame Index")

References

Zouhir: : Zouhir, Y., & Ouni, K. (2016). Feature Extraction Method for Improving Speech Recognition in Noisy Environments. J. Comput. Sci., 12, 56-61.

spafe.features.msrcc

spafe.features.pncc