This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) without increasing calculation cost. It consists of three components: (i) multi-domain loss (MDL), (ii) bridging operation, which couples the individual instrument networks, and (iii) combination loss (CL). MDL enables the taking advantage of the frequency- and time-domain representations of audio signals. We modify the target network, i.e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information. MDL is then applied to the combinations of the output sources as well as each independent source, hence we called it CL. MDL and CL can easily be applied to many DNN-based separation methods as they are merely loss functions that are only used during training and do not affect the inference step. Bridging operation does not increase the number of learnable parameters in the network. Experimental results showed that the validity of Open-Unmix (UMX) and densely connected dilated DenseNet (D3Net) extended with our X-scheme, respectively called X-UMX and X-D3Net, by comparing them with their original versions. We also verified the effectiveness of X-scheme in a large-scale data regime, showing its generality with respect to data size. X-UMX Large (X-UMXL), which was trained on large-scale internal data and used in our experiments, is newly available at https://github.com/asteroid-team/asteroid/tree/master/egs/musdb18/X-UMX.

翻译：本文提出交叉方案（X-scheme），用于在不增加计算成本的情况下提升基于深度神经网络（DNN）的音乐源分离（MSS）性能。该方案包含三个组成部分：（i）多域损失（MDL）、（ii）桥接操作（将各个乐器网络耦合）以及（iii）组合损失（CL）。MDL能充分利用音频信号的频域和时域表示。我们通过为每个输出乐器添加桥接路径以实现信息共享，从而修改目标网络（即原始DNN-MSS的网络架构）。随后将MDL应用于输出源的组合以及每个独立源，因此称为CL。MDL和CL可轻易应用于多种基于DNN的分离方法，因为它们仅作为训练阶段使用的损失函数，不影响推理步骤。桥接操作不会增加网络中可学习参数的数量。实验结果表明，将我们的X-scheme扩展至Open-Unmix（UMX）和密集连接扩张DenseNet（D3Net）后分别得到的X-UMX与X-D3Net，相较于原始版本具有有效性。我们还在大规模数据场景下验证了X-scheme的有效性，证明了其相对于数据规模的泛化能力。本研究实验中使用的X-UMX Large（X-UMXL）基于大规模内部数据训练，现已可从https://github.com/asteroid-team/asteroid/tree/master/egs/musdb18/X-UMX 获取。