Many tasks in music information retrieval (MIR) involve weakly aligned data,
where exact temporal correspondences are unknown. The connectionist temporal
classification (CTC) loss is a standard technique to learn feature
representations based on weakly aligned training data. However, CTC is limited
to discrete-valued target sequences and can be difficult to extend to
multi-label problems. In this article, we show how soft dynamic time warping
(SoftDTW), a differentiable variant of classical DTW, can be used as an
alternative to CTC. Using multi-pitch estimation as an example scenario, we
show that SoftDTW yields results on par with a state-of-the-art multi-label
extension of CTC. In addition to being more elegant in terms of its algorithmic
formulation, SoftDTW naturally extends to real-valued target sequences.

本文介绍了一种处理音乐信息检索中涉及的弱对齐数据的新技术 —— 软动态时间规整（SoftDTW），这是一种可微分的经典动态时间规整（DTW）变体，相较于 CTC 算法在算法形式上更加优雅，能够自然地扩展到实值目标序列，并在多标签问题方面达到了 CTC 最新扩展的水平，文中以多音调估计为例具体论述了该技术。