Music Genre Classification Using Audio Data

Sowmya Natarajan, Shivansh Saxena,  Ishan Bhardwaj, Hussain Hakeem
Dept of Electronics & Communication Engineering S.R.M. Institute of Science & Technology Chennai, India.
sowmyan1@srmist.edu.in
ss0885@srmist.edu.in
ib8884@srmist.edu.in
hh1834@srmist.edu.in
DOI: 10.46793/BISEC25.466N

 

ABSTRACT: Music genre classification involves automatically categorizing music tracks into specific genres based on audio features. Key techniques include fea-ture extraction, focusing on attributes like rhythm, timbre, and pitch. Algorithms such as K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), and Random Forests are commonly used to perform the classification task. K-NN relies on similarity measures between tracks, SVM separates genres by finding optimal hyperplanes, and Random Forests combine multiple decision trees for robust classification. These methods enhance music recommendation and organ-ization systems by enabling efficient genre identification. We achieved highest accuracy of 87% with random forest, 71% with svm and 27% with KNN .

KEYWORDS: Music genre classification, Support Vector Machines (SVM), Ran-dom Forests

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