Music Representation Learning

Music Representation Learning is a computational framework within algorithmic composition that systematically acquires and structures symbolic or audio-based musical features through machine learning methods, distinguishing itself by enabling high-level musical understanding and generation through data-driven latent representations.

530

Publications

29.9K

Citations

1.2K

Authors

388

Institutions

Publications per year

2017–2026

315

Authors

1.2K

  1. YY
    Yi‐Hsuan Yang

    Academia Sinica, Taiwan

    32 publications · 2.2K citations

    25

    h-index

  2. DE
    Douglas Eck

    Université de Montréal, Canada

    17 publications · 1.9K citations

    16

    h-index

  3. GW
    Gerhard Widmer

    Johannes Kepler University of Linz, Austria

    21 publications · 663 citations

    16

    h-index

  4. AP
    Adam P. Roberts

    University College London, United Kingdom

    15 publications · 1.4K citations

    14

    h-index

  5. JE
    Jesse Engel

    Google (United States), United States

    15 publications · 1.4K citations

    14

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 107 publications · 12.1K citations

    38

    h-index

  2. Zenodo (CERN European Organization for Nuclear Research)

    Repository · 30 publications · 1.1K citations

    18

    h-index

  3. Journal of New Music Research

    14 publications · 639 citations

    13

    h-index

  4. 8

    h-index

  5. IEEE Access

    8 publications · 216 citations

    8

    h-index

1–5