Published Paper Details:

With applications ranging from playlist creation and music recommendation to content-based music retrieval, music genre categorization is a crucial task in the field of music information retrieval. This review article offers a thorough analysis of the most recent approaches, difficulties, and trends in music genre classification. We examine several approaches, such as audio-based and lyrics-based approaches, hybrid models that incorporate multiple modalities, and features utilized for automatic genre classification. The study analyzes how music genre classification has changed over time, moving from manual feature extraction and genre taxonomy, two old methodologies, to more recent advancements in deep learning and neural networkbased techniques. We investigate the effects of massive music datasets and data augmentation methods on the effectiveness of genre classifiers. Furthermore, we discuss the evaluation of classification models, including widely used metrics and benchmark datasets. Cross-genre classification, fine-grained genre classification, and the incorporation of cultural and contextual aspects into the categorization process are just a few of the difficulties and open research problems in the discipline that are discussed. We also stress how important it is for users to trust and accept music genre classification models, which is essential for implementation in real-world applications. The study also discusses how music genre classification can be used in realworld contexts in fields like music streaming, recommendation engines, and music analysis. Last but not least, it provides information about possible future prospects for study in this field, including making use of cutting-edge tools like neural network search, federated learning, and the incorporation of user feedback for customized genre classification. With the goal of illuminating the most recent developments and the difficulties that still remain in the effort to more fully comprehend and automate the classification of musical genres, this study promises to be an invaluable resource for researchers, practitioners, and amateurs interested in music genre classification.