We use cookies on this website to distinguish you from other users. We use this data to improve our content experience and for targeted advertising. By continuing to use this website you consent to our use of cookies. For more information, please see our Cookie Policy.

spafe.features.ngcc#

Description : Normalized Gammachirp Cepstral Coefficients (NGCCs) 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>.

<<<<<<< HEAD spafe.features.ngcc.ngcc(sig: numpy.ndarray, fs: int = 16000, num_ceps=13, pre_emph: bool = True, pre_emph_coeff: float = 0.97, window: Optional[spafe.utils.preprocessing.SlidingWindow] = None, nfilts: int = 24, nfft: int = 512, low_freq: Optional[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[numpy.ndarray] = None, conversion_approach: Literal['Glasberg'] = 'Glasberg') → numpy.ndarray[source]#

spafe.features.ngcc.ngcc(sig: ndarrayfs: int = 16000num_ceps=13pre_emph: bool = Truepre_emph_coeff: float = 0.97window: Optional[SlidingWindow] = Nonenfilts: int = 24nfft: int = 512low_freq: Optional[float] = 0high_freq: Optional[float] = Nonescale: Literal['ascendant', 'descendant', 'constant'] = 'constant'dct_type: int = 2use_energy: bool = Falselifter: Optional[int] = Nonenormalize: Optional[Literal['mvn', 'ms', 'vn', 'mn']] = Nonefbanks: Optional[ndarray] = Noneconversion_approach: 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