Blind music source separation has been a popular and active subject of research in both the music information retrieval and signal processing communities. To counter the lack of available multi-track data for supervised model training, a data augmentation method that creates artificial mixtures by combining tracks from different songs has been shown useful in recent works. Following this light, we examine further in this paper extended data augmentation methods that consider more sophisticated mixing settings employed in the modern music production routine, the relationship between the tracks to be combined, and factors of silence. As a case study, we consider the separation of violin and piano tracks in a violin piano ensemble, evaluating the performance in terms of common metrics, namely SDR, SIR, and SAR. In addition to examining the effectiveness of these new data augmentation methods, we also study the influence of the amount of training data. Our evaluation shows that the proposed mixing-specific data augmentation methods can help improve the performance of a deep learning-based model for source separation, especially in the case of small training data.

本文介绍了一种针对现代音乐制作中复杂混音设置的数据增强方法，并以小提琴琴、钢琴伴奏的音乐合奏为例，研究了这种方法对盲音源分离模型的影响。研究表明，所提出的混音特异性数据增强方法可以显著提高基于深度学习的盲音源分离模型的性能。

混合特定数据增强技术以提高盲人小提琴/钢琴源分离效果