Author:
Thành Chu Bá,Loan Trinh Van,Thuy Dao Thi Le
Abstract
We can say that music in general is an indispensable spiritual food in human life. For Vietnamese people, folk music plays a very important role, it has entered the minds of every Vietnamese person right from the moment of birth through lullabies for children. In Vietnam, there are many different types of folk songs that everyone loves, and each has many different melodies. In order to archive and search music works with a very large quantity, including folk songs, it is necessary to automatically classify and identify those works. This paper presents the method of determining the feature parameters and then using the convolution neural network (CNN) to classify and identify some Vietnamese folk tunes as Quanho and Cheo. Our experimental results show that the average highest classification and identification accuracy are 99.92% and 97.67%, respectivel.
Publisher
Publishing House for Science and Technology, Vietnam Academy of Science and Technology (Publications)
Reference89 articles.
1. [1] Cunningham, Padraig, and S. J. Delany, “k-Nearest neighbor classifiers,” Multiple Classifier Systems, vol. 34, no. 8, pp. 1-17, 2007.
2. [2] Y. Sazaki, A. Aramadhan, “Rock genre classification using k-nearest neighbor,” Proceeding of The 1st International Conference on Computer Science and Engineering, pp. 81-84, 2014.
3. [3] Ghahramani, Zoubin. “An introduction to hidden Markov models and Bayesian networks,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 15, no. 1, pp. 9-42, 2001. https://doi.org/10.1142/9789812797605 0002
4. [4] X. Shao, C. Xu and M. S. Kankanhalli, “Unsupervised classification of music genre using hidden Markov model,” 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004, pp. 2023-2026 vol.3. Doi: 10.1109/ICME.2004.1394661
5. [5] J. Reed and C.H. Lee. “A study on music genre classification based on universal acoustic models,” In Proceedings of the International Conference on Music Information Retrieval, pages 89-94, 2006.
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