Deep Learning Approach for Singer Voice Classification of Vietnamese Popular Music
This work addresses the problem of singer identification for music information retrieval in Vietnamese popular music, representing an incremental improvement over existing methods on this specific dataset.
The paper tackles singer voice classification for Vietnamese popular music by proposing a neural network method using vocal segment detection and singing voice separation, achieving an accuracy of 92.84% on a dataset of 300 songs from 18 singers.
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.