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Da-TACOS: A dataset for cover song identification and understanding

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dc.contributor.author Yesiler, Furkan
dc.contributor.author Tralie, Chris
dc.contributor.author Correya, Albin Andrew
dc.contributor.author Silva, Diego F.
dc.contributor.author Tovstogan, Philip
dc.contributor.author Gómez Gutiérrez, Emilia, 1975-
dc.contributor.author Serra, Xavier
dc.date.accessioned 2019-11-07T09:07:22Z
dc.date.available 2019-11-07T09:07:22Z
dc.date.issued 2019
dc.identifier.citation Yesiler F, Tralie C, Correya A, Silva DF, Tovstogan P, Gómez E, Serra X. Da-TACOS: A dataset for cover song identification and understanding. In: Proceedings of the 20th Conference of the International Society for Music Information Retrieval (ISMIR 2019): 2019 Nov 4-8; Delft, The Netherlands. [Canada]: ISMIR; 2019. p. 327-34.
dc.identifier.uri http://hdl.handle.net/10230/42771
dc.description Comunicació presentada a: 20th annual conference of the International Society for Music Information Retrieval (ISMIR) celebrat del 4 al 8 de novembre de 2019 a Delft, Països Baixos.
dc.description.abstract This paper focuses on Cover Song Identification (CSI), an important research challenge in content-based Music Information Retrieval (MIR). Although the task itself is interesting and challenging for both academia and industry scenarios, there are a number of limitations for the advancement of current approaches. We specifically address two of them in the present study. First, the number of publicly available datasets for this task is limited, and there is no publicly available benchmark set that is widely used among researchers for comparative algorithm evaluation. Second, most of the algorithms are not publicly shared and reproducible, limiting the comparison of approaches. To overcome these limitations we propose Da-TACOS, a DaTAset for COver Song Identification and Understanding, and two frameworks for feature extraction and benchmarking to facilitate reproducibility. Da-TACOS contains 25K songs represented by unique editorial metadata plus 9 low- and mid-level features pre-computed with open source libraries, and is divided into two subsets. The Cover Analysis subset contains audio features (e.g. key, tempo) that can serve to study how musical characteristics vary for cover songs. The Benchmark subset contains the set of features that have been frequently used in CSI research, e.g. chroma, MFCC, beat onsets etc. Moreover, we provide initial benchmarking results of a selected number of state-of-the-art CSI algorithms using our dataset, and for reproducibility, we share a GitHub repository containing the feature extraction and benchmarking frameworks.
dc.description.sponsorship This work is partially 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, and by TROMPA, the Horizon 2020 project 770376-2.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher International Society for Music Information Retrieval (ISMIR)
dc.relation.ispartof Proceedings of the 20th Conference of the International Society for Music Information Retrieval (ISMIR 2019): 2019 Nov 4-8; Delft, The Netherlands. [Canada]: ISMIR; 2019.
dc.rights © Furkan Yesiler, Chris Tralie, Albin Correya, Diego F. Silva, Philip Tovstogan, Emilia Gómez, Xavier Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Furkan Yesiler, Chris Tralie, Albin Correya, Diego F. Silva, Philip Tovstogan, Emilia Gómez, Xavier Serra. “Da-TACOS: A Dataset for Cover Song Identification and Understanding”, 20th International Society for Music Information Retrieval Conference, Delft, The Netherlands, 2019.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title Da-TACOS: A dataset for cover song identification and understanding
dc.type info:eu-repo/semantics/conferenceObject
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/770376
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/765068
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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