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arXiv · 2102.12111

Deep Learning Approach for Singer Voice Classification of Vietnamese Popular Music

Toan Pham Van·Ngoc N. Tran·Ta Minh Thanh
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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 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.

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BibTeXRIS

Toan Pham Van, Ngoc N. Tran, Ta Minh Thanh. 2021-02-24. Deep Learning Approach for Singer Voice Classification of Vietnamese Popular Music. https://doi.org/10.1145/3368926.3369700

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