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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 2022-09-22T12:20:19Z
dc.date.available 2022-09-22T12:20:19Z
dc.date.issued 2022-09-22
dc.identifier.uri http://hdl.handle.net/10230/54157
dc.description This work has been accepted at the 23rd International Society for Music Information Retrieval Conference (ISMIR 2022), at Bengaluru, India. December 4-8, 2022.
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 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.language.iso eng
dc.rights © A, Morsi, X. Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: A, Morsi, X. Serra, “Bottlenecks and Solutions for Audio to Score Alignment Research”, in Proc. of the 23rd Int. Society for Music Information Retrieval Conf., Bengaluru, India, 2022.
dc.rights.uri https://creativecommons.org/licenses/by/4.0
dc.title Bottlenecks and solutions for audio to score alignment research
dc.type info:eu-repo/semantics/preprint
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-111403GB-I00
dc.rights.accessRights info:eu-repo/semantics/openAccess


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