A Lightweight Music Source Separation Model with Graph Convolution Network

With the rapid advancement of deep neural networks, there has been a significant improvement in the performance of music source separation methods. However, most of them primarily focus on improving their separation performance, while ignoring the issue of model size in the real-world environments. For the application in the real-world environments, in this paper, we propose a lightweight network combined with the Graph convolutional network Attention (GCN_A) module for Music Source Separation (G-MSS), which includes an Encoder and four Decoders, each of them outputs a target music source. The G-MSS network adopts both time-domain and frequency-domain L1 losses. The ablation study verifies the effectiveness of our designed GCN Attention (GCN_A) module and multiple Decoders, and also make a visualization analysis of the main components in the G-MSS network. Comparing with the other 13 methods on the MUSDB18 dataset, our proposed G-MSS achieves comparable separation performance while maintaining the lower amount of parameters.