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Inspired by the recent progress in self-supervised learning for computervision that generates supervision using data augmentations, we explore a newgeneral-purpose audio representation learning approach. We propose learninggeneral-purpose audio representation from a single audio segment withoutexpecting relationships between different time segments of audio samples. Toimplement this principle, we introduce Bootstrap Your Own Latent (BYOL) forAudio (BYOL-A, pronounced "viola"), an audio self-supervised learning methodbased on BYOL for learning general-purpose audio representation. Unlike mostprevious audio self-supervised learning methods that rely on agreement ofvicinity audio segments or disagreement of remote ones, BYOL-A createscontrasts in an augmented audio segment pair derived from a single audiosegment. With a combination of normalization and augmentation techniques,BYOL-A achieves state-of-the-art results in various downstream tasks. Extensiveablation studies also clarified the contribution of each component and theircombinations.

BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation