Comprehensive Music Genre Classification: Exploring Advanced Machine Learning Techniques with the GTZAN Dataset

Music is a universal language that comes in a wide variety of genres to suit different interests and moods. In order to achieve this organization, music genre classification—the process of automatically classifying a piece of music according to its auditory content—is essential. A multi-class music genre identification model has been constructed in the proposed study, and ten distinct classifiers, including K-NN, Random Forest, Cat Boost, XG Boost, Decision Tree, etc., have been tested. A popular benchmark dataset for classifying musical genres, the GTZAN dataset offers a wealth of information for developing and assessing classification algorithms. In contrast, CatBoost reports the best outcome, with an F1-score of 0.89.