Bottlenecks and solutions for audio to score alignment research

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  • dc.contributor.author Morsi, Alia
  • dc.contributor.author Serra, Xavier
  • dc.date.accessioned 2023-04-11T06:42:51Z
  • dc.date.available 2023-04-11T06:42:51Z
  • dc.date.issued 2022
  • dc.description Comunicació presentada a 23nd International Society for Music Information Retrieval Conference (ISMIR 2022), celebrat del 4 al 8 de desembre de 2022 a Bangalore, Índia.
  • dc.description.abstract Although audio to score alignment is a classic Music Information Retrieval problem, it has not been defined uniquely with the scope of musical scenarios representing its core. The absence of a unified vision makes it difficult to pinpoint its state-of-the-art and determine directions for improvement. To get past this bottleneck, it is necessary to consolidate datasets and evaluation methodologies to allow comprehensive benchmarking. In our review of prior work, we demonstrate the extent of variation in problem scope, datasets, and evaluation practices across audio to score alignment research. To circumvent the high cost of creating large-scale datasets with various instruments, styles, performance conditions, and musician proficiency levels from scratch, the research community could generate ground truth approximations from non-audio to score alignment datasets which include a temporal mapping between a music score and its corresponding audio. We show a methodology for adapting the Aligned Scores and Performances dataset, created originally for beat tracking and music transcription. We filter the dataset semi-automatically by applying a set of Dynamic Time Warping based Audio to Score Alignment methods using out-of-the-box Chroma and Constant-Q Transform extraction algorithms, suitable for the characteristics of the piano performances of the dataset. We use the results to discuss the limitations of the generated ground truths and data adaptation method. While the adapted dataset does not provide the necessary diversity for solving the initial problem, we conclude with ideas for expansion, and identify future directions for curating more comprehensive datasets through data adaptation, or synthesis.
  • dc.description.sponsorship This research was carried out under the project Musical AI - PID2019-111403GB-I00/AEI/10.13039/501100011033, funded by the Spanish Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación.
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Morsi A, Serra X. Bottlenecks and solutions for audio to score alignment research. In: Rao P, Murthy H, Srinivasamurthy A, Bittner R, Caro Repetto R, Goto M, Serra X, Miron M, editors. Proceedings of the 23nd International Society for Music Information Retrieval Conference (ISMIR 2022); 2022 Dec 4-8; Bengaluru, India. [Canada]: International Society for Music Information Retrieval; 2022. p. 272-9. DOI: 10.5281/zenodo.7343047
  • dc.identifier.doi http://dx.doi.org/10.5281/zenodo.7343047
  • dc.identifier.isbn 978-1-7327299-2-6
  • dc.identifier.uri http://hdl.handle.net/10230/56439
  • dc.language.iso eng
  • dc.publisher International Society for Music Information Retrieval (ISMIR)
  • dc.relation.ispartof Rao P, Murthy H, Srinivasamurthy A, Bittner R, Caro Repetto R, Goto M, Serra X, Miron M, editors. Proceedings of the 23nd International Society for Music Information Retrieval Conference (ISMIR 2022); 2022 Dec 4-8; Bengaluru, India. [Canada]: International Society for Music Information Retrieval; 2022. p. 272-9.
  • dc.relation.isreferencedby https://github.com/Alia-morsi/asa_benchmarks
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-111403GB-I00
  • dc.rights © A. Morsi and X. Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.other Música
  • dc.title Bottlenecks and solutions for audio to score alignment research
  • dc.type info:eu-repo/semantics/conferenceObject
  • dc.type.version info:eu-repo/semantics/publishedVersion

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