Open Access

ARTICLE

Music Genre Classification Using DenseNet and Data Augmentation

Dao Thi Le Thuy¹, Trinh Van Loan^2,*, Chu Ba Thanh³, Nguyen Hieu Cuong¹

1 Faculty of Information Technology, University of Transport and Communications, Hanoi, 100000, Vietnam
2 School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, 100000, Vietnam
3 Faculty of Information Technology, University of Technology and Education Hung Yen, 160000, Vietnam

* Corresponding Author: Trinh Van Loan. Email:

Computer Systems Science and Engineering 2023, 47(1), 657-674. https://doi.org/10.32604/csse.2023.036858

Received 13 October 2022; Accepted 16 January 2023; Issue published 26 May 2023

Abstract

It can be said that the automatic classification of musical genres plays a very important role in the current digital technology world in which the creation, distribution, and enjoyment of musical works have undergone huge changes. As the number of music products increases daily and the music genres are extremely rich, storing, classifying, and searching these works manually becomes difficult, if not impossible. Automatic classification of musical genres will contribute to making this possible. The research presented in this paper proposes an appropriate deep learning model along with an effective data augmentation method to achieve high classification accuracy for music genre classification using Small Free Music Archive (FMA) data set. For Small FMA, it is more efficient to augment the data by generating an echo rather than pitch shifting. The research results show that the DenseNet121 model and data augmentation methods, such as noise addition and echo generation, have a classification accuracy of 98.97% for the Small FMA data set, while this data set lowered the sampling frequency to 16000 Hz. The classification accuracy of this study outperforms that of the majority of the previous results on the same Small FMA data set.

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Keywords

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