MDCNN-SID: Multi-scale Dilated Convolution Network for Singer Identification
Abstract
Most singer identification methods are processed in the frequency domain, which potentially leads to information loss during the spectral transformation. In this paper, instead of the frequency domain, we propose an end-to-end architecture that addresses this problem in the waveform domain. An encoder based on Multi-scale Dilated Convolution Neural Networks (MDCNN) was introduced to generate wave embedding from the raw audio signal. Specifically, dilated convolution layers are used in the proposed method to enlarge the receptive field, aiming to extract song-level features. Furthermore, skip connection in the backbone network integrates the multi-resolution acoustic features learned by the stack of convolution layers. Then, the obtained wave embedding is passed into the following networks for singer identification. In experiments, the proposed method achieves comparable performance on the benchmark dataset of Artist20, which significantly improves related works.
Cite
@article{arxiv.2004.04371,
title = {MDCNN-SID: Multi-scale Dilated Convolution Network for Singer Identification},
author = {Xulong Zhang and Jianzong Wang and Ning Cheng and Jing Xiao},
journal= {arXiv preprint arXiv:2004.04371},
year = {2022}
}
Comments
Accepted by IJCNN2022 (The 2022 International Joint Conference on Neural Networks)