Using Support Vector Machine for Music Genre Classification

Abstract

Musical genres are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music/audio information retrieval. Genre categorization for music/audio has traditionally been performed manually. Automatic music genre classification is very useful for music indexing and retrieval. In this paper, we present an efficient and effective automatic music genre classification approach. Music genre classification is processed in two parts, feature extraction and classification. A set of feature is extracted and used to characterize music content. A multilayer classifier based on support vector machine is applied to music genre class-ification. Support vector machines are used to obtain the optimal class boundaries between different genres of music by learning from training data .The classification results of the proposed feature set has 93% accuracy rate improvement in the multilayer SVM.