Very short feature vector for music genre classiciation based on distance metric lerning

Abstract

In our study, a very short feature vector, obtained from low dimensional projection and already developed audio features, is used for music genre classification problem. A long feature vector based on the concatenation of various features is generally used in music genre classification system. Our objective is to find a short feature vector, and we applied a distance metric learning algorithm in order to reduce the dimensionality of feature vector with a little performance degradation. In our experiments based on two widely-used dataset, dimension reduction based on distance metric learning is very effective, and we can get over 80% of accuracy with only 10-dimensional feature vector.