Classification of music genre using support vector machine and convolutional neural network
Department of Mathematics, Centurion University of Technology and Management, Odisha, India.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(03), 1009–1019
Publication history:
Received on 01 February 2024; revised on 06 March 2024; accepted on 09 March 2024
Abstract:
Music is an art of combining rhythms, melodies, harmony, and form and also an art of expressing content. Almost all people in this world love Music. This depends on the person and what type of Music he likes to listen to. Either it will be Classical, melodious rock Hip-hop, or some other class of genre. The task of classifying music files based on genre is extremely difficult, especially in the area of MIR (Music Information Retrieval). The approach involves deep learning, where CNN and SVM models are trained end-to-end using a spectrogram to classify a signal's genre label. Every person has a different choice. In this paper, a set of Music is classified into different types of genres with the help of machine learning. A set of 1000 audio files containing 10 types of genres, with 100 audio files each has been taken. The audio features have been extracted using the Spectral roll-off, Chroma features, and zero Crossing Rate. Then, the model was predicted using different algorithms like Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). After the model is predicted using the algorithms, CNN outperformed SVM and gave an accuracy of 96.2%.
Keywords:
Music genre; Machine Learning Model; Feature extraction; Support Vector Machine; Convolutional Neural Network.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0