Multi-pitch estimation is a decades-long research problem involving the
detection of pitch activity associated with concurrent musical events within
multi-instrument mixtures. Supervised learning techniques have demonstrated
solid performance on more narrow characterizations of the task, but suffer from
limitations concerning the shortage of large-scale and diverse polyphonic music
datasets with multi-pitch annotations. We present a suite of self-supervised
learning objectives for multi-pitch estimation, which encourage the
concentration of support around harmonics, invariance to timbral
transformations, and equivariance to geometric transformations. These
objectives are sufficient to train an entirely convolutional autoencoder to
produce multi-pitch salience-grams directly, without any fine-tuning. Despite
training exclusively on a collection of synthetic single-note audio samples,
our fully self-supervised framework generalizes to polyphonic music mixtures,
and achieves performance comparable to supervised models trained on
conventional multi-pitch datasets.

多音高估计中的多音高估计，监督式学习技术，自监督式学习目标，多声乐曲数据集，卷积自编码器

实现全自监督多音高估计

Toward Fully Self-Supervised Multi-Pitch Estimation

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。