Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks

Understand

Representation learning from unlabeled data has been of major interest in artificial intelligence research.

• While self-supervised speech representation learning has been popular in the speech research community, very few works have comprehensively analyzed audio representation learning for non-speech audio tasks.
• In this paper, we propose a self-supervised audio representation learning method and apply it to a variety of downstream non-speech audio tasks.
• We combine the well-known wav2vec 2.0 framework, which has shown success in self-supervised learning for speech tasks, with parameter-efficient conformer architectures.