Different methods and techniques for features extraction from audio
A micro framework for cough signal processing
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| 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. |
from csp import SpectrogramFeatures# path of the cough audiosp = SpectrogramFeatures('cough_sound_9412.m4a')data = sp.spectrogram_data()
from csp import AudioAugmentation# Audio_augmentation speed tuningAudio_aug = AudioAugmentation.speed_tuning(data['signal'])
# Audio augmentation time shiftingaug = AudioAugmentation.time_shifting(data['signal'])
If you find this repository useful, please cite our project:
@misc{CSP2019,author = {Pal, Ankit},title = {{Cough Signal Processing ( csp )}},year = {2019},month = {June},url = {https://github.com/coughresearch/Cough-signal-processing},}
<img width=850” src=”./Images/Audio Feature Classification.PNG”>
