- Database Used: http://opihi.cs.uvic.ca/sound/genres.tar.gz [1.2GB]
- We are using kernel SVM not 'Linear' because our dataset will not be linearly saperable if we visualize.So Here I am using pyAudioAnanlysis [Reference: https://github.com/tyiannak/pyAudioAnalysis ] library to extract all 34 feature from music file.
- When our features are extracted in a vector format we will feed these data to SVM model,but as mentioned before it is not possible to saperate linearly.So we are using kernel SVM.
- Features extracted using pyaudioanalysis library.
| Feature Name | Description |
|---|---|
| Zero-Crossing Rate | The rate of sign changes of the signal during the duration of a particular frame. |
| Energy | The sum of squares of the signal values, normalized by the respective frame length. |
| Entropy of Energy | The entropy of sub-frames normalized energies. It can be interpreted as a measure of abrupt changes. |
| Spectral Centroid | The center of gravity of the spectrum. |
| Spectral Spread | The second central moment of the spectrum. |
| Spectral Entropy | The entropy of the normalized spectral energies for a set of sub-frames. |
| Spectral Flux | The squared difference between the normalized magnitudes of the spectra of the two successive frames. |
| Spectral Rolloff | The frequency below which 90% of the magnitude distribution of the spectrum is concentrated. |
| MFCCs (9-21) | Mel Frequency Cepstral Coefficients form a cepstral representation where the frequency bands are not linear but distributed according to the mel-scale. |
| Chroma Vector (22-33) | A 12-element representation of the spectral energy where the bins represent the 12 equal-tempered pitch classes of western-type music (semitone spacing). |
| Chroma Deviation | The standard deviation of the 12 chroma coefficients. |
- Kernel SVM use different type of function which transform data into new dimension where data is linearly saperable.
- Here is good reference: http://crsouza.com/2010/03/17/kernel-functions-for-machine-learning-applications/
- I have utilized '.wav' format but we can be used to convert all type of extension to .wav format.
So after training our SVM classifier we will test on any file.
- I have selected 'isSignificant' level which is minimum probability to classify our test file.[In my code : 0.6]
- Base-view
- Upload file format enforcing
- Prediction of uploaded file
- Output when it fails to predict based on significance set
Note: This is a utilisation of library to perform classification to get understanding of Kernel SVM.