Machine Learning Approaches and Analysis for Bangla Music Genre Classification

The challenge of classifying the genres of Bangla music has been given a machine learning based technique in this study. This paper has explored the use of feature extraction techniques for selecting highly co-related features and evaluated the performance of gradient boosting classifiers, CatBoost, and XGBoost, on a comprehensive Bangla music dataset. Our paper's proposed method achieves remarkable accuracy scores of 74% using CatBoost and XGBoost. This study emphasizes how machine learning approaches have enormous promise to address the difficult job of classifying genres in Bangla music. By using advanced feature extraction techniques and powerful gradient boosting classifiers,the proposed method were able to achieve promising results in this challenging task. The implications of the work can be extended to the development of intelligent music rec-ommendation systems and automated music tagging, facilitating enhanced user experiences in exploring and discovering Bangla music genres. As the field of machine learning continues to evolve, this research contributes valuable insights and avenues for future advancements in music genre classification and related domains.