Automatic chord-scale recognition using harmonic pitch class profiles

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• dc.contributor.author Demirel, Emir
• dc.contributor.author Bozkurt, Baris
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2019-06-05T11:16:23Z
• dc.date.available 2019-06-05T11:16:23Z
• dc.date.issued 2019
• dc.description Comunicació presentada a: 16th Sound & Music Computing Conference celebrada del 23 al 31 de maig de 2019 a Málaga, Espanya.
• dc.description.abstract This study focuses on the application of different computational methods to carry out a ”modal harmonic analysis” for Jazz improvisation performances by modeling the concept of chord-scales. The Chord-Scale Theory is a theoretical concept that explains the relationship between the harmonic context of a musical piece and possible scale types to be used for improvisation. This work proposes different computational approaches for the recognition of the chordscale type in an improvised phrase given the harmonic context. We have curated a dataset to evaluate different chordscale recognition approaches proposed in this study, where the dataset consists of around 40 minutes of improvised monophonic Jazz solo performances. The dataset is made publicly available and shared on freesound.org. To achieve the task of chord-scale type recognition, we propose one rule-based, one probabilistic and one supervised learning method. All proposed methods use Harmonic Pitch Class Profile (HPCP) features for classification. We observed an increase in the classification score when learned chordscale models are filtered with predefined scale templates indicating that incorporating prior domain knowledge to learned models is beneficial. This study has its novelty in presenting a first computational analysis on chord-scales in the context of Jazz improvisation.
• dc.description.sponsorship This work is supported by ERC funded TECSOME project. The author E.D received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowsa-Curie grant agreement No. 765068.
• dc.format.mimetype application/pdf
• dc.identifier.citation Demirel E, Bozkurt B, Serra X. Automatic chord-scale recognition using harmonic pitch class profiles. In: Barbancho I, Tardón LJ, Peinado A, Barbancho AM, editors. Proceedings of the 16th Sound & Music Computing Conference; 2019 May 28-31; Málaga, Spain. [Málaga]: SMC; 2019. p. 72-9.
• dc.identifier.isbn 978-84-09-08518-7
• dc.identifier.uri http://hdl.handle.net/10230/41706
• dc.language.iso eng
• dc.publisher Sound & Music Computing Conference
• dc.relation.ispartof Barbancho I, Tardón LJ, Peinado A, Barbancho AM, editors. Proceedings of the 16th Sound & Music Computing Conference; 2019 May 28-31; Málaga, Spain. [Málaga]: SMC; 2019.
• dc.relation.isreferencedby https://freesound.org/people/emirdemirel/packs/24075/
• dc.relation.isreferencedby https://github.com/emirdemirel/Chord-ScaleDetection
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/768530
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/765068
• dc.rights © 2018 Emir Demirel et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/3.0/
• dc.title Automatic chord-scale recognition using harmonic pitch class profiles
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion