Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/85242
Title: Automatic identification of some Vietnamese folk songs Cheo and Quanho using deep neural networks
Authors: Chu, Ba Thanh
Trinh, Van Loan
Dao, Thi Le Thuy
Keywords: Identification
Classification
Folk songs
Vietnamese
Cheo
Quanho
CNN
LSTM
CRNN
Issue Date: 2022
Series/Report no.: Journal of Computer Science and Cybernetics;Vol.38, No.01 .- P.63-83
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 tunes. 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), Long-Short Term Memory networks (LSTM), and Convolutional Recurrent Neural Network (CRNN) 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%, respectively.
URI: https://dspace.ctu.edu.vn/jspui/handle/123456789/85242
ISSN: 1813-9663
Appears in Collections:Tin học và Điều khiển học (Journal of Computer Science and Cybernetics)

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