Audio Self-supervised Learning

Audio Self-Supervised Learning is a machine learning paradigm that belongs to the broader category of self-supervised learning, distinguished by its focus on learning representations from raw audio signals without human-annotated labels by designing pretext tasks that leverage the intrinsic structure of audio data.

127

Publications

8K

Citations

417

Authors

128

Institutions

Publications per year

2017–2026

125

Authors

417

  1. AB
    Alexei Baevski

    Meta (Israel), Israel

    9 publications · 900 citations

    9

    h-index

  2. MA
    Michael Auli

    Meta (Israel), Israel

    6 publications · 729 citations

    6

    h-index

  3. NH
    Noboru Harada

    NTT (Japan), Japan

    4 publications · 111 citations

    5

    h-index

  4. YO
    Yasunori Ohishi

    NTT (Japan), Japan

    4 publications · 111 citations

    5

    h-index

  5. KK
    Kunio Kashino

    NTT (Japan), Japan

    4 publications · 111 citations

    5

    h-index

1–5 of 417

Venues

  1. arXiv (Cornell University)

    Repository · 27 publications · 3.6K citations

    19

    h-index

  2. 9

    h-index

  3. Interspeech 2022

    Conference · 4 publications · 243 citations

    5

    h-index

  4. 5

    h-index

  5. 4

    h-index

1–5