Automatic Note-Level Score-to-Performance Alignments in the ASAP Dataset-Reference-Cited by-同舟云学术

Automatic Note-Level Score-to-Performance Alignments in the ASAP Dataset

Published:2023 Issue:1 Volume:6 Page:27-42
ISSN:2514-3298
Container-title:Transactions of the International Society for Music Information Retrieval
language:en
Short-container-title:

Author:

Peter Silvan David^ORCID,Cancino-Chacón Carlos Eduardo^ORCID,Foscarin Francesco^ORCID,McLeod Andrew Philip^ORCID,Henkel Florian,Karystinaios Emmanouil^ORCID,Widmer Gerhard^ORCID

Publisher

Ubiquity Press, Ltd.

Subject

Linguistics and Language,Library and Information Sciences,Music,Museology

Reference43 articles.

1. Arzt, A. (2016). Flexible and Robust Music Tracking. PhD thesis, Johannes Kepler University Linz, Linz, Austria.

2. Computational models of expressive music performance: A comprehensive and critical review;Frontiers in Digital Humanities,2018

3. Partitura: A python package for symbolic music processing,2022

4. An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music;Machine Learning,2017

5. Improved score-performance alignment algorithms on polyphonic music,2014

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