Wavebyol: self-supervised learning for audio representation from raw waveforms

HIGHLIGHTS

  • who: SUNGHYUN KIM and collaborators from the School of Robotics, Kwangwoon University, have published the research: WaveBYOL: Self-Supervised Learning for Audio Representation From Raw Waveforms, in the Journal: (JOURNAL)
  • what: The authors propose the WaveBYOL model which can learn general-purpose audio representations directly from raw waveforms based on the bootstrap your own latent (BYOL) approach a Siamese neural network architecture. The authors assess the representations learned by WaveBYOL by conducting experiments with seven audio downstream tasks under both frozen-model evaluation and fine-tuning settings. In the audio domain, recent studies have been . . .

     

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