vocal/non vocal segmentation using SVM classifier

Hi all,

I'm working on a singer recognition project that identifies singer in a music recording via Multifeature Statistical Singer Modelling. The architecture of the system that I want to implement, can be found in the following link:

https://ieeexplore.ieee.org/Xplore/login.jsp?url=/stamp/stamp.jsp?arnumber=1617537&isnumber=33902

At this stage, I trying to apply vocal/non vocal segmentation using SVM classifier as it shown in Music Preprocessing module in the page 3 of the article posted above. I use Matlab Sptoolbox.

I exracted features for each frame (Spectral centroid, Spectral flux, Zero crossings, and Low energy) and tried to train the SVM binary classifier in 2-dimensional space, for example: Zero Crossing Rate vs Spectral centroid, but the error percentage was too high (around 30%).

I also extracted MFCC coefficients from vocal/non vocal regions as suggested in the article, but I don’t understand how can I train the 2-dimensional binary SVM classifier using 14 dimensional features vector, and is it even possible to perform the classification in 2D space.

Here are some of the articles I used for reference, but they didn't give an answer or perhaps I didn't understood the point.
• Mel frequency cepstral coefficients for music modeling

https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.9216

• LOCATING SINGING VOICE SEGMENTS WITHIN MUSIC SIGNALS

https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.3067

Separation of vocals from polyphonic audio recordings

https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.75.5580


I would like if someone could suggest me any other articles on this issue or explain shortly how to train the binary SVM classifier with MFCC coefficients in order to apply vocal/non vocal segmentation.


Thanks in advance,

Rodion