Cough Signal Processing ( csp )

A micro framework for cough singal processing

Contribute and Support

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 }