A fairly straightforward approach for music source separation is to train
independent models, wherein each model is dedicated for estimating only a
specific source. Training a single model to estimate multiple sources generally
does not perform as well as the independent dedicated models. However,
Conditioned U-Net (C-U-Net) uses a control mechanism to train a single model
for multi-source separation and attempts to achieve a performance comparable to
that of the dedicated models. We propose a multi-channel U-Net (M-U-Net)
trained using a weighted multi-task loss as an alternative to the C-U-Net. We
investigate two weighting strategies for our multi-task loss: 1) Dynamic
Weighted Average (DWA), and 2) Energy Based Weighting (EBW). DWA determines the
weights by tracking the rate of change of loss of each task during training.
EBW aims to neutralize the effect of the training bias arising from the
difference in energy levels of each of the sources in a mixture. Our methods
provide three-fold advantages compared to C-UNet: 1) Fewer effective training
iterations per epoch, 2) Fewer trainable network parameters (no control
parameters), and 3) Faster processing at inference. Our methods achieve
performance comparable to that of C-U-Net and the dedicated U-Nets at a much
lower training cost.

通过动态加权平均和基于能量的加权策略的多通道 U-Net 模型能够有效地实现多音源分离，且训练代价较低。