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 }
