Machine Learning For Acoustics

Machine Learning for Acoustics is an academic discipline that applies machine learning methodologies to the analysis, interpretation, and modeling of acoustic signals, distinguishing itself by its focus on learning patterns and structures from auditory data to solve tasks such as sound classification, source separation, and acoustic event detection.

666

Publications

29.6K

Citations

2K

Authors

711

Institutions

Publications per year

2017–2026

436

Authors

2K

  1. TV
    Tuomas Virtanen

    21 publications · 1.6K citations

    18

    h-index

  2. TH
    Toni Heittola

    13 publications · 1.1K citations

    12

    h-index

  3. MD
    Mark D. Plumbley

    Queen Mary University of London, United Kingdom

    10 publications · 410 citations

    11

    h-index

  4. AM
    Annamaria Mesaros

    Tampere University, Finland

    11 publications · 982 citations

    11

    h-index

  5. BW
    Björn W. Schuller

    Imperial College London, United Kingdom

    10 publications · 856 citations

    10

    h-index

1–5 of 2K

Venues

  1. arXiv (Cornell University)

    Repository · 27 publications · 956 citations

    18

    h-index

  2. The Journal of the Acoustical Society of America

    JASA · 19 publications · 1.2K citations

    15

    h-index

  3. IEEE/ACM Transactions on Audio Speech and Language Processing

    TASLP · 11 publications · 636 citations

    13

    h-index

  4. IEEE Access

    12 publications · 1.1K citations

    12

    h-index

  5. Sensors

    11 publications · 450 citations

    12

    h-index

1–5