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- W3201143670 abstract "Inspired by the recent progress in self-supervised learning for computer vision that generates supervision using data augmentations, we explore a new general-purpose audio representation learning approach. We propose learning general-purpose audio representation from a single audio segment without expecting relationships between different time segments of audio samples. To implement this principle, we introduce Bootstrap Your Own Latent (BYOL) for Audio (BYOL-A, pronounced “viola”), an audio self-supervised learning method based on BYOL for learning general-purpose audio representation. Unlike most previous audio self-supervised learning methods that rely on agreement of vicinity audio segments or disagreement of remote ones, BYOL-A creates contrasts in an augmented audio segment pair derived from a single audio segment. With a combination of normalization and augmentation techniques, BYOL-A achieves state-of-the-art results in various downstream tasks. Extensive ablation studies also clarified the contribution of each component and their combinations." @default.
- W3201143670 created "2021-09-27" @default.
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- W3201143670 date "2021-07-18" @default.
- W3201143670 modified "2023-10-17" @default.
- W3201143670 title "BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation" @default.
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- W3201143670 doi "https://doi.org/10.1109/ijcnn52387.2021.9534474" @default.
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