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
Parents
853
Software Engineering for Machine Learning: A Case Study
Saleema Amershi, Andrew Begel, Christian Bird et al. · 2019 · 1.1K citations
1–5 of 853
3.9K
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4
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4
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4
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1–5 of 3.9K
1.5K
Harvard University
Cambridge, United States
14 h-index
29 publications
3.8K citations
Massachusetts General Hospital
Boston, United States
9 h-index
16 publications
1.3K citations
University of Pennsylvania
Philadelphia, United States
9 h-index
16 publications
1.1K citations

Brigham and Women's Hospital
Boston, United States
8 h-index
9 publications
406 citations

Imperial College London
London, United Kingdom
8 h-index
9 publications
331 citations

Symbiosis International University
Pune, India
7 h-index
13 publications
1.9K citations
Stanford University
Stanford, United States
7 h-index
14 publications
842 citations
University of Washington
Seattle, United States
7 h-index
13 publications
446 citations
1–8 of 1.5K
Repository · 15 publications · 546 citations
15
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11 publications · 2.4K citations
12
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11 publications · 703 citations
12
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6 publications · 188 citations
11
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Repository · 8 publications · 407 citations
8
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1–5