Machine Listening

Machine Listening is a computational framework within generative sound art that enables machines to analyze, interpret, and respond to audio signals through algorithmic processing, distinguishing itself from related auditory models by its focus on generative, context-aware, and often creative sonic interactions.

44

Publications

2.8K

Citations

116

Authors

56

Institutions

Publications per year

2017–2026

19

Authors

116

  1. JP
    Juan Pablo Bello

    New York University, United States

    6 publications · 560 citations

    6

    h-index

  2. JS
    Justin Salamon

    New York University, United States

    4 publications · 514 citations

    4

    h-index

  3. RR
    Robert Rowe

    Perfect Harmony Health, United States

    3 publications · 360 citations

    3

    h-index

  4. BW
    Björn W. Schuller

    Imperial College London, United Kingdom

    3 publications · 84 citations

    3

    h-index

  5. RF
    Richard F. Lyon

    Google (United States), United States

    2 publications · 155 citations

    2

    h-index

1–5 of 116

Venues

  1. Computer Music Journal

    4 publications · 213 citations

    4

    h-index

  2. Applied Sciences

    2 publications · 75 citations

    2

    h-index

  3. Journal of the Audio Engineering Society

    AES · 2 publications · 31 citations

    2

    h-index

  4. Choice Reviews Online

    CRO · 2 publications · 225 citations

    2

    h-index

  5. Advanced Intelligent Systems

    1 publications · 20 citations

    1

    h-index

1–5