Automatic Singing Evaluation without Reference Melody Using Bi-dense Neural Network
Ning Zhang, Tao Jiang, Feng Deng, Yan Li
Abstract
Automatic singing evaluation without reference melody has long been a difficult problem. This paper aims to pilot a novel data driven approach to tackle this artistic problem. We constructed a large scale dataset and designed an innovative Bi-Dense neural network which can address this task efficiently. Though the singing evaluation is quite a subjective task and depends a lot on listeners' preferences, we showed that a specific group has consistency on the singing evaluations, and it is possible to train a model to learn the subjective preferences of this group. In this paper, a large amount of singing clips and corresponding human gradings were collected. And an elaborate designed Bi-DenseNet was trained to discriminate the good singings from the poor ones. The experiments demonstrated the proposed network performs better than the existing networks for singing evaluation task.
BibTeX
@inproceedings{icassp2019_automaticsinging,
title = {Automatic Singing Evaluation without Reference Melody Using Bi-dense Neural Network},
author = {Ning Zhang and Tao Jiang and Feng Deng and Yan Li},
booktitle = {ICASSP 2019},
year = {2019}
}