Classification of music genre using support vector machine and convolutional neural network

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%.