Music Genre Classification Using African Buffalo Optimization

Abstract

In the discipline of Music Information Retrieval (MIR), categorizing music files according to their genre is a difficult process. Music genre classification is an important multimedia research domain for classification of music databases. In the proposed method music genre classification using features obtained from audio data is proposed. The classification is done using features extracted from the audio data of popular online repository namely GTZAN, ISMIR 2004 and Latin Music Dataset (LMD). The features highlight the differences between different musical styles. In the proposed method, feature selection is performed using an African Buffalo Optimization (ABO), and the resulting features are employed to classify the audio using Back Propagation Neural Networks (BPNN), Support Vector Machine (SVM), Naïve Bayes, decision tree and kNN classifiers. Performance evaluation reveals that, ABO based feature selection strategy achieves an average accuracy of 82% with mean square error (MSE) of 0.003 when used with neural network classifier.