Abstract: This paper presents a comprehensive study on automatic music genre classification using machine learning techniques applied to the widely-used GTZAN dataset. We extracted 13 audio features including spectral and temporal characteristics to classify music into 10 distinct genres. Our Random Forest classifier achieved 78.2% accuracy, outperforming baseline models including Support Vector Machines (72.4%) and K-Nearest Neighbors (69.1%). Feature importance analysis revealed that Mel-Frequency Cepstral Coefficients (MFCCs) and spectral centroid are the most discriminative features.........
Key Word: Music Information Retrieval, Genre Classification, Audio Feature Extraction, Random Forest, GTZAN Dataset.
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