Towards Proper Contrastive Self-supervised Learning Strategies For Music Audio Representation

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...

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Bibliographic Details
Published in:arXiv.org 2022-07
Main Authors: Choi, Jeong, Jang, Seongwon, Cho, Hyunsouk, Chung, Sehee
Format: Article
Language:English
Subjects:
Information retrieval
Music
Representations
Self-supervised learning
Supervised learning
Online Access:Get full text
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Summary: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.
ISSN:2331-8422