The common research goal of self-supervised learning is to extract a general
representation which an arbitrary downstream task would benefit from. In this
work, we investigate music audio representation learned from different
contrastive self-supervised learning schemes and empirically evaluate the
embedded vectors on various music information retrieval (MIR) tasks where
different levels of the music perception are concerned. We analyze the results
to discuss the proper direction of contrastive learning strategies for
different MIR tasks. We show that these representations convey a comprehensive
information about the auditory characteristics of music in general, although
each of the self-supervision strategies has its own effectiveness in certain
aspect of information.

本文研究了不同对比自监督学习方案学习的音乐音频表示，并在不同的音乐信息检索任务中经验性地评估嵌入向量，以分析结果并讨论对不同 MIR 任务的适当对比学习策略方向，表明这些表示传达了关于音乐的听觉特征的综合信息，尽管每个自我监督策略在某些方面的信息方面有效。