用条件UNet提升音乐分离精度，参数更少效果更好。
A Conditioned UNet for Music Source Separation
- 在UNet中引入条件机制，通过音频查询指定目标声部
- 在多个任务上比基准方法高1dB以上信噪比，参数量减半
- 适合需要灵活声部分离的音乐处理场景
本文提出一种用于音乐源分离（MSS）的条件化UNet——QSCNet。传统MSS多采用多输出神经网络，需预定义乐器词表；而条件化方法仅需提供目标声部的音频查询即可完成分离，无需固定词表，更贴近真实应用场景。此前研究认为UNet不适用于条件化分离，但该论文通过在稀疏压缩网络中集成条件模块，构建了新型条件化UNet。基于MoisesDb数据集评估显示，QSCNet在多个任务中性能优于当前最佳方法Banquet，信噪比提升超过1dB，同时参数量不足其一半。
原文摘要 · Abstract (English)
In this paper we propose a conditioned UNet for Music Source Separation (MSS). MSS is generally performed by multi-output neural networks, typically UNets, with each output representing a particular stem from a predefined instrument vocabulary. In contrast, conditioned MSS networks accept an audio query related to a stem of interest alongside the signal from which that stem is to be extracted. Thus, a strict vocabulary is not required and this enables more realistic tasks in MSS. The potential of conditioned approaches for such tasks has been somewhat hidden due to a lack of suitable data, an issue recently addressed with the MoisesDb dataset. A recent method, Banquet, employs this dataset with promising results seen on larger vocabularies. Banquet uses Bandsplit RNN rather than a UNet and the authors state that UNets should not be suitable for conditioned MSS. We counter this argument and propose QSCNet, a novel conditioned UNet for MSS that integrates network conditioning elements in the Sparse Compressed Network for MSS. We find QSCNet to outperform Banquet by over 1dB SNR on a couple of MSS tasks, while using less than half the number of parameters.
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