Music Genre Classification

This study explores the feasibility of utilizing K Means clustering for music genre classification.Leveraging feature extraction and selection techniques, music tracks are systematically categorized into distinct genres based on their acoustic attributes.The research rigorously evaluates the efficacy of this methodology using a diverse set of evaluation metrics, juxtaposing its performance against alternative classification approaches.By delving into the application of K Means clustering in music genre classification, this study contributes significantly to the advancement of automated genre tagging systems within digital music libraries.The findings gleaned from this investigation offer valuable insights into the performance and suitability of K Means clustering for music classification tasks, thereby guiding future research endeavors in refining music genre classification algorithms.Through meticulous experimentation and analysis, this study elucidates the intricacies and nuances of employing K Means clustering in the context of music genre classification.By scrutinizing various aspects of the classification process, including feature extraction methods, clustering parameters, and evaluation metrics, a comprehensive understanding of the methodology's efficacy is attained.The outcomes of this research not only enrich the existing body of knowledge in music classification but also provide practical implications for developing more robust and accurate genre classification systems.Ultimately, this study serves as a stepping stone towards advancing the automation of genre tagging processes, fostering more efficient management and organization of digital music repositories.