Music Genre Classification Algorithm Based on Dynamic Frame Analysis and Support Vector Machine

This paper proposes a new music genre classification algorithm based on dynamic music frame analysis and support vector machine (SVM). The dynamic music frame analysis could cover the long-term and the short-term music genre features which can represent the time-varying behavior of music signals. The music genre features used in this paper are mel-frequency cepstral coefficient (MFCC) and log energy with dynamic frame length. The dynamic music frame analysis will be applied to train an optimized non-linear decision rule for music genre classifier via SVM. Experimental results show that the proposed new music genre classification algorithm could achieve the average classification accuracy rate of 98% for the six different music genres, including classic, dance, lullaby, Bossa, piano, and blue.