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Different methods and techniques for features extraction from audio

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Cough Signal Processing ( csp )

A micro framework for cough singal processing

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GitHub license GitHub commit PRs Welcome

Features

  • Spectrogram features extraction
  • Contiguous features
  • Cough event detection
  • Experiments on noise removal, Silence in cough sounds
  • Applying different types of filters
  • Audio augmentation techniques
Feature ID Feature Name Description
1 Zero Crossing Rate The rate of sign-changes of the signal during the duration of a particular frame.
2 Energy The sum of squares of the signal values, normalized by the respective frame length.
3 Entropy of Energy The entropy of sub-frames' normalized energies. It can be interpreted as a measure of abrupt changes.
4 Bispectrum Score (BGS) 3rd order spectrum of the signal is known as the bispectrum.
5 Non-gaussianity score(NGS) NGS gives the measure of non-gaussianity of a given segment of data.
6 Formant frequencies (FF) A formant is the spectral shaping that results from an acoustic resonance of the human vocal tract.
7 log energy (LogE) The log energy for every subsegment
8 kurtosis (Kurt) kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable.
9 MFCCs Mel Frequency Cepstral Coefficients form a cepstral representation where the frequency bands are not linear but distributed according to the mel-scale.
10 MFCC delta, delta2 Delta-MFCC and Delta-Delta-MFCC are used to extract the features of speakers.
11 Skewness skewness is a measure of the asymmetry of the probability distribution
12 Power Spectral Density (PSD) A Power Spectral Density (PSD) is the measure of signal's power content versus frequency.
13 Linear Predictive Coding (LPC) Representing the spectral envelope of a digital signal of speech in compressed form
14 Continuous Wavelet Transform (CWT) provides an overcomplete representation of a signal by letting the translation and scale parameter of the wavelets vary continuously.

More features and suggestions are welcome.

Quick Start

from csp import SpectrogramFeatures

# path of the cough audio
sp   = SpectrogramFeatures('cough_sound_9412.m4a')
data = sp.spectrogram_data()

output

Audio augmentation techniques

Speed tuning

from csp import AudioAugmentation

# Audio_augmentation speed tuning
Audio_aug = AudioAugmentation.speed_tuning(data['signal'])

output

Time shifting

# Audio augmentation time shifting
aug = AudioAugmentation.time_shifting(data['signal'])

output

Feature extraction { @thileepanp }

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