kishanmistri / music-genre-classification

Classification base on kernel SVM

Music-Genre-Classification in action

Classification based on kernel SVM

Description

• 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 Details:

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.

Some information about kernel SVM:

• 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]

Screens

• 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.