  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.
