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spafe.features.rplp#

Description : (Rasta) Perceptual linear prediction coefficents (RPLPs/PLPs) 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.rplp.plp(sig: ndarray, fs: int = 16000, order: int = 13, pre_emph: bool = False, 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', lifter: Optional[int] = None, normalize: Optional[Literal['mvn', 'ms', 'vn', 'mn']] = None, fbanks: Optional[ndarray] = None, conversion_approach: Literal['Wang', 'Tjomov', 'Schroeder', 'Terhardt', 'Zwicker', 'Traunmueller'] = 'Wang') → ndarray[source]#

Compute Perceptual linear prediction coefficents according to [Hermansky] and [Ajibola].

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).
order (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”).
lifter (int) – apply liftering if specified. (Default is None).
normalize (str) – apply normalization if approach specified. (Default is None).
fbanks (numpy.ndarray) – filter bank matrix. (Default is None).
conversion_approach (str) – bark scale conversion approach. (Default is “Wang”).

Returns

2d array of PLP features (num_frames x order)

Return type

(numpy.ndarray)

Tip

normalize : can take the following options [“mvn”, “ms”, “vn”, “mn”].
conversion_approach : can take the following options [“Tjomov”,”Schroeder”, “Terhardt”, “Zwicker”, “Traunmueller”, “Wang”]. Note that the use of different options than the ddefault can lead to unexpected behavior/issues.

Note

../_images/plps.png — Architecture of perceptual linear prediction coefﬁcients extraction algorithm.#

Examples

from scipy.io.wavfile import read
from spafe.features.rplp import plp
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 plps
plps = plp(sig,
           fs=fs,
           pre_emph=0,
           pre_emph_coeff=0.97,
           window=SlidingWindow(0.03, 0.015, "hamming"),
           nfilts=128,
           nfft=1024,
           low_freq=0,
           high_freq=fs/2,
           lifter=0.9,
           normalize="mvn")

# visualize features
show_features(plps, "Perceptual linear predictions", "PLP Index", "Frame Index")

spafe.features.rplp.rplp(sig: ndarray, fs: int = 16000, order: int = 13, pre_emph: bool = False, 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', lifter: Optional[int] = None, normalize: Optional[Literal['mvn', 'ms', 'vn', 'mn']] = None, fbanks: Optional[ndarray] = None, conversion_approach: Literal['Wang', 'Tjomov', 'Schroeder', 'Terhardt', 'Zwicker', 'Traunmueller'] = 'Wang') → ndarray[source]#

Compute rasta Perceptual linear prediction coefficents according to [Hermansky] and [Ajibola].

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).
order (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”).
lifter (int) – apply liftering if specified. (Default is None).
normalize (str) – apply normalization if approach specified. (Default is None).
fbanks (numpy.ndarray) – filter bank matrix. (Default is None).
conversion_approach (str) – bark scale conversion approach. (Default is “Wang”).

Returns

2d array of rasta PLP features (num_frames x order)

Return type

(numpy.ndarray)

Tip

normalize : can take the following options [“mvn”, “ms”, “vn”, “mn”].
conversion_approach : can take the following options [“Tjomov”,”Schroeder”, “Terhardt”, “Zwicker”, “Traunmueller”, “Wang”]. Note that the use of different options than the ddefault can lead to unexpected behavior/issues.

Note

../_images/rplps.png — Architecture of rasta perceptual linear prediction coefﬁcients extraction algorithm.#

Examples

from scipy.io.wavfile import read
from spafe.features.rplp import rplp
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 rplps
rplps = rplp(sig,
             fs=fs,
             pre_emph=0,
             pre_emph_coeff=0.97,
             window=SlidingWindow(0.03, 0.015, "hamming"),
             nfilts=128,
             nfft=1024,
             low_freq=0,
             high_freq=fs/2,
             lifter=0.9,
             normalize="mvn")

# visualize features
show_features(rplps, "Rasta perceptual linear predictions", "PLP Index", "Frame Index")

References

Ajibola(1,2): : Ajibola Alim, S., & Khair Alang Rashid, N. (2018). Some Commonly Used Speech Feature Extraction Algorithms. From Natural to Artificial Intelligence - Algorithms and Applications. doi:10.5772/intechopen.80419

spafe.features.psrcc

spafe.features.spfeats