Evaluation of Music Features for PUK Kernel Based Genre Classification

Since music conveys as well as evokes a wealth of emotions in a listener, there has been tremendous research and commercial development to automatically organize music using smart machine learning techniques. In this work, various features are extracted from the music signal for an effective representation to aid in genre classification. The feature set comprises of dynamic, rhythm, tonal, and spectral features comprising a total of 144 features. The size of feature set is further reduced to 39 features using correlation-based feature selection mechanism to remove the correlated features. Support vector machine classifier is used to train the genre classification system with a flexible Pearson Universal Kernel (PUK) that can adapt its behavior to various functions (from linear to Gaussian). The reduced feature set, consisting mostly rhythm and spectral features, significantly outperforms the complete feature set leading to an accuracy of 82% for classifying 5 genres.