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Music Genre Classification Using GA-Induced Minimal Feature-Set

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2 Author(s)
Nayak, S. ; Dept. of Electr. Eng., IIT Kanpur, Kanpur, India ; Bhutani, A.

We propose a genetic algorithm-based feature-selection method for music genre classification that not only increases the efficiency of standard classifiers, but also reduces the feature space to a bare-minimum. While previous works have been more focused on finding near-optimal features devoid of noise, we go for a modified fitness function capable of finding both the near-optimal and the near-minimal feature subset for classification. In addition to an enhanced performance, our model can also reduce the computational load for ill-formed sets and has the flexibility to incorporate trade-offs between efficiency and computational load. We finally demonstrate that the modified GA is capable of bringing about an 80% reduction in the feature space dimension at similar classification rates.

Published in:

Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2011 Third National Conference on

Date of Conference:

15-17 Dec. 2011

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