Abstract
This survey extensively studies music genre classification, a critical task in music information retrieval, to automatically categorize audio recordings into various genres. It provides a comprehensive review of approaches, methodologies, and recent advancements in genre classification from audio data. Scholars and practitioners in the field will find this study to be a valuable resource as it covers various aspects of the discipline, including feature extraction, classification methods, dataset exploration, evaluation metrics, and recent developments. The survey aims to enhance the understanding of music genre classification and foster further research and progress in the field by critically evaluating state-of-the-art techniques discussed in research papers, discussing their strengths and limitations, and providing a comprehensive overview of the field.
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