Music Genre Classification Using K-Nearest Neighbor and Mel-Frequency Cepstral Coefficients

Abstract

Music genre classification plays a pivotal role in organizing and accessing vast music collections, enhancing user experiences, and enabling efficient music recommendation systems. This study focuses on employing the K-Nearest Neighbors (KNN) algorithm in conjunction with Mel-Frequency Cepstral Coefficients (MFCCs) for accurate music genre classification. MFCCs extract essential spectral features from audio signals, which serve as robust representations of music characteristics. The proposed approach achieves a commendable classification accuracy of 80%, showcasing the effectiveness of KNN-MFCC fusion. Nevertheless, the challenge of overlapping genres, particularly rock and country, demands special attention due to their shared acoustic attributes. The inherent similarities between these genres often lead to misclassification, hampering accuracy. To address this issue, an enhanced feature engineering strategy is devised, leveraging deeper insights into the subtle nuances that differentiate rock and country music. Additionally, a refined KNN distance metric and neighbor selection mechanism are introduced to further refine classification decisions. Experimental results underscore the effectiveness of the refined approach in mitigating genre overlap issues, significantly enhancing classification accuracy for rock and country genres. This study contributes to the advancement of music genre classification techniques, offering an innovative solution for handling overlapping genres and demonstrating the potential of KNN-MFCC synergy in achieving accurate and refined genre classification.

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