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spafe.utils.preprocessing#

Description : Preprocessing utils 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>.

class spafe.utils.preprocessing.SlidingWindow(win_len: float = 0.025, win_hop: float = 0.01, win_type: Literal['hanning', 'bartlet', 'kaiser', 'blackman', 'hamming'] = 'hamming')[source]#

Bases: object

Sliding widow class.

Parameters

win_len (float) – window length in sec. (Default is 0.025).
win_hop (float) – step between successive windows in sec. (Default is 0.01).
win_type (float) – window type to apply for the windowing. (Default is “hamming”).

win_hop: float = 0.01#

win_len: float = 0.025#

win_type: Literal['hanning', 'bartlet', 'kaiser', 'blackman', 'hamming'] = 'hamming'#

spafe.utils.preprocessing.framing(sig: ndarray, fs: int = 16000, win_len: float = 0.025, win_hop: float = 0.01) → Tuple[ndarray, int][source]#

transform a signal into a series of overlapping frames (= Frame blocking) as described in [Malek-framing-blog].

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).
win_len (float) – window length in sec. (Default is 0.025).
win_hop (float) – step between successive windows in sec. (Default is 0.01).

Returns

(numpy.ndarray) : array of frames.
(int) : frame length.

Return type

(tuple)

Note

Uses the stride trick to accelerate the processing.

References

Malek-framing-blog: : Malek A., Signal framing, 25.01.2022, https://superkogito.github.io/blog/2020/01/25/signal_framing.html

spafe.utils.preprocessing.pre_emphasis(sig: ndarray, pre_emph_coeff: float = 0.97) → ndarray[source]#

perform preemphasis on the input signal.

Parameters

sig (numpy.ndarray) – input signal.
coeff (float) – preemphasis coefficient. 0 is no filter. (Default is 0.97).

Returns

pre-empahsised signal.

Return type

(numpy.ndarray)

Note

\[y[t] = x[t] - \alpha \times x[t-1]\]

spafe.utils.preprocessing.stride_trick(a: ndarray, stride_length: int, stride_step: int) → ndarray[source]#

apply framing using the stride trick from numpy.

Parameters

a (numpy.ndarray) – signal array.
stride_length (int) – length of the stride.
stride_step (int) – stride step.

Returns

blocked/framed array.

Return type

(numpy.ndarray)

Note

You can refer to numpy documentation of this stride trick here: https://numpy.org/doc/stable/reference/generated/numpy.lib.stride_tricks.sliding_window_view.html

spafe.utils.preprocessing.windowing(frames: ndarray, frame_len: int, win_type: Literal['hanning', 'bartlet', 'kaiser', 'blackman', 'hamming'] = 'hamming') → ndarray[source]#

generate and apply a window function to avoid spectral leakage [Malek-windowing-blog].

Parameters

frames (numpy.ndarray) – array including the overlapping frames.
frame_len (int) – frame length.
win_type (str) – type of window to use. (Default is “hamming”).

Returns

windowed frames.

Return type

(numpy.ndarray)

References

Malek-windowing-blog: : Malek, A. Specctral leakage, 2022.03.13, https://superkogito.github.io/blog/2020/03/13/spectral_leakage_and_windowing.html

spafe.utils.preprocessing.zero_handling(x: ndarray) → ndarray[source]#

handle the issue with zero values if they are exposed to become an argument for any log function.

Parameters: x (numpy.ndarray) – input vector.
Returns: vector with zeros substituted with epsilon values.
Return type: (numpy.ndarray)

spafe.utils.filters

spafe.utils.spectral