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spafe.features.msrcc#

Description : Magnitude based Spectral Root Cepstral Coefficients (MSRCCs) 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.msrcc.msrcc(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: float = 0, high_freq: Optional[float] = None, scale: Literal['ascendant', 'descendant', 'constant'] = 'constant', gamma: float = - 0.14285714285714285, 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['Oshaghnessy', 'Lindsay'] = 'Oshaghnessy') → ndarray[source]#

Compute the Magnitude-based Spectral Root Cepstral Coefﬁcients (MSRCC) from an audio signal according to [Tapkir].

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”).
gamma (float) – power coefficient for resulting energies (Default -1/7).
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 specified. (Default is None).
fbanks (numpy.ndarray) – filter bank matrix. (Default is None).
conversion_approach (str) – approach to use for conversion to the mel scale. (Default is “Oshaghnessy”).

Returns

2d array of MSRCC 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 [“Oshaghnessy”, “Lindsay”]. Note that the use of different options than the default can lead to unexpected behavior/issues.

Note

../_images/msrccs.png — Architecture of magnitude based spectral root cepstral coefﬁcients extraction algorithm.#

Examples

from scipy.io.wavfile import read
from spafe.features.msrcc import msrcc
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 msrccs
msrccs  = msrcc(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(msrccs, "Magnitude based Spectral Root Cepstral Coefficients", "MSRCC Index", "Frame Index")

References

Tapkir: : P. A. Tapkir, A. T. Patil, N. Shah and H. A. Patil, “Novel Spectral Root Cepstral Features for Replay Spoof Detection,” 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2018, pp. 1945-1950, doi: 10.23919/APSIPA.2018.8659746.

spafe.features.mfcc

spafe.features.ngcc