Investigating the efficacy of music version retrieval systems for setlist identification

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• dc.contributor.author Yesiler, Furkan
• dc.contributor.author Molina, Emilio
• dc.contributor.author Serrà, Joan
• dc.contributor.author Gómez Gutiérrez, Emilia, 1975-
• dc.date.accessioned 2023-03-01T13:48:54Z
• dc.date.available 2023-03-01T13:48:54Z
• dc.date.issued 2021
• dc.description Comunicació presentada a 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021), celebrat del 6 a l'11 de juny de 2021 de manera virtual.
• dc.description.abstract The setlist identification (SLI) task addresses a music recognition use case where the goal is to retrieve the metadata and timestamps for all the tracks played in live music events. Due to various musical and non-musical changes in live performances, developing automatic SLI systems is still a challenging task that, despite its industrial relevance, has been under-explored in the academic literature. In this paper, we propose an end-to-end workflow that identifies relevant metadata and timestamps of live music performances using a version identification system. We compare 3 of such systems to investigate their suitability for this particular task. For developing and evaluating SLI systems, we also contribute a new dataset that contains 99.5 h of concerts with annotated metadata and timestamps, along with the corresponding reference set. The dataset is categorized by audio qualities and genres to analyze the performance of SLI systems in different use cases. Our approach can identify 68% of the annotated segments, with values ranging from 35% to 77% based on the genre. Finally, we evaluate our approach against a database of 56.8 k songs to illustrate the effect of expanding the reference set, where we can still identify 56% of the annotated segments.
• dc.description.sponsorship This work is supported by the MIP-Frontiers project, the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068.
• dc.format.mimetype application/pdf
• dc.identifier.citation Yesiler F, Molina E, Serrà J, Gómez E. Investigating the efficacy of music version retrieval systems for setlist identification. In: 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021): proceedings; 2021 Jun 6-11; Toronto, Canada. [Piscataway]: IEEE; 2021. p. 541-5. DOI: 10.1109/ICASSP39728.2021.9414603
• dc.identifier.doi http://dx.doi.org/10.1109/ICASSP39728.2021.9414603
• dc.identifier.issn 1520-6149
• dc.identifier.uri http://hdl.handle.net/10230/55989
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021): proceedings; 2021 Jun 6-11; Toronto, Canada. [Piscataway]: IEEE; 2021. p. 541-5.
• dc.relation.isreferencedby https://github.com/furkanyesiler/setlist_id
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/765068
• dc.rights © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/ICASSP39728.2021.9414603
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Setlist identification
• dc.subject.keyword version identification
• dc.subject.keyword live performance monitoring
• dc.subject.keyword music recognition
• dc.title Investigating the efficacy of music version retrieval systems for setlist identification
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion