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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.

Carnatic music is rich in its own style but more complex in the way the notes are arranged and rendered. Each Carnatic raga possess definite rules to be followed to frame its musical notes. But the significance of musical notes arrangement of the Carnatic music is unknown. This paper provides an attempt to find the significant relationship between the combinations of musical notes with the musical parameters. The objective of this work is to find the influence of various musical parameters on the Carnatic raga notes using Neutrosophic Cognitive Maps (NCMs). An analysis is made and the influences of musical parameters are cross checked with those of the Neutrosophic Cognitive Maps (NCMs). The values of the musical features of each raga are obtained using MIR MATLAB Toolbox.

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