Music genre classification has been a toughest task in the area of music information retrieval (MIR). Classification of genre can be important to clarify some genuine fascinating issues, such as, making songs references, discovering related songs, finding societies who will like that particular song. The inspiration behind the research is to find the appropriate machine learning algorithm that predict the genres of music utilizing k-nearest neighbor (k-NN) and Support Vector Machine (SVM). GTZAN dataset is the frequently used dataset for the classification music genre. The Mel Frequency cepstral coefficients (MFCC) is utilized to extricate features for the dataset. From results we found that k-NN classifier gave more exact results compared to support vector machine classifier. If the training data is bigger than number of features, k-NN gives better outcomes than SVM. SVM can only identify limited set of patterns. KNN classifier is more powerful for the classification of music genre.
Machine Learning, Mel Frequency Cepstral Coefficients, k-Nearest Neighbors, Support Vector Machine
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