Ai-based Practice

AI-Based Practice is a scholarly contribution in the field of Music Practice Technology that integrates artificial intelligence methodologies to innovate, systematize, or evaluate music practice processes, thereby distinguishing itself from traditional, non-computational approaches by its reliance on adaptive algorithms, data-driven models, or machine learning techniques to enhance musical skill development, performance accuracy, or pedagogical effectiveness.

853

Publications

51.9K

Citations

3.9K

Authors

1.5K

Institutions

Publications per year

2017–2026

805

Authors

3.9K

  1. MT
    Masayuki Tsuneki

    Niigata University, Japan

    4 publications · 80 citations

    4

    h-index

  2. SK
    Saskia K. Nagel

    RWTH Aachen University, Germany

    3 publications · 103 citations

    4

    h-index

  3. AS
    Aruna Seneviratne

    UNSW Sydney, Australia

    4 publications · 368 citations

    4

    h-index

  4. PN
    Pubudu N. Pathirana

    Deakin University, Australia

    4 publications · 368 citations

    4

    h-index

  5. AT
    An Tang

    Université de Montréal, Canada

    4 publications · 654 citations

    4

    h-index

1–5 of 3.9K

Venues

  1. arXiv (Cornell University)

    Repository · 15 publications · 546 citations

    15

    h-index

  2. Journal of Medical Internet Research

    11 publications · 2.4K citations

    12

    h-index

  3. Scientific Reports

    11 publications · 703 citations

    12

    h-index

  4. Diagnostics

    6 publications · 188 citations

    11

    h-index

  5. SSRN Electronic Journal

    Repository · 8 publications · 407 citations

    8

    h-index

1–5