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Deep learning approach for singer voice classification of Vietnamese popular music

Van Pham, T. and Quang, N.T.N. and Thanh, T.M. (2019) Deep learning approach for singer voice classification of Vietnamese popular music. In: 10th International Symposium on Information and Communication Technology, SoICT 2019, 4 December 2019 through 6 December 2019.

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Abstract

Singer voice classification is a meaningful task in the digital era. With a huge number of songs today, identifying a singer is very helpful for music information retrieval, music properties indexing, and so on. In this paper, we propose a new method to identify the singer's name based on analysis of Vietnamese popular music. We employ the use of vocal segment detection and singing voice separation as the pre-processing steps. The purpose of these steps is to extract the singer's voice from the mixture sound. In order to build a singer classifier, we propose a neural network architecture working with Mel Frequency Cepstral Coefficient (MFCC) as extracted input features from said vocal. To verify the accuracy of our methods, we evaluate on a dataset of 300 Vietnamese songs from 18 famous singers. We achieve an accuracy of 92.84% with 5-fold stratified cross-validation, the best result compared to other methods on the same data set. © 2019 Association for Computing Machinery.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1145/3368926.3369700
Uncontrolled Keywords: Classification (of information); Information retrieval; Network architecture; Cross validation; Input features; Learning approach; Mel-frequency cepstral coefficients; Music information retrieval; Popular music; Pre-processing step; Singing voice separations; Deep learning
Additional Information: Conference code: 156141. Language of original document: English. All Open Access, Green.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9199

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