International Journal For Multidisciplinary Research

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Music Genre Classification Using Machine Learning

Author(s) Junnuthula Rajesh Reddy, Karri Kusuma Phani Sree Reddy, Mohammad Mansoor Ali, Dr. U. Sesadri
Country India
Abstract Music genre classification is a subfield of audio and music analysis in which machine learning and data analysis techniques are used to automatically categorize music tracks into predefined genre categories. In this study, we explore music genre classification using three machine learning algorithms: Support Vector Machine (SVM), Naive Bayes, and k-Nearest Neighbors (k-NN). Our dataset spans diverse music genres, from mainstream to niche, and we employ feature extraction techniques like rhythm-based features. Evaluation metrics, including accuracy, precision, recall, and F1-score, assess model performance. Cross-validation ensures robustness, while addressing imbalanced data is considered. Our findings offer insights into the suitability of SVM, Naive Bayes, and k-NN for music genre classification, providing valuable guidance for audio analysis practitioners. This research sets the stage for further exploration of advanced modeling techniques and real-world challenges in audio classification.
Keywords Music Genre, Classification, Support Vector Machine, Naïve Bayes, k-Nearest Neighbors.
Field Engineering
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-23
Cite This Music Genre Classification Using Machine Learning - Junnuthula Rajesh Reddy, Karri Kusuma Phani Sree Reddy, Mohammad Mansoor Ali, Dr. U. Sesadri - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.8857
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.8857
Short DOI https://doi.org/gtfmvk
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