Improved automatic instrumentation role classification and loop activation transcription
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- dc.contributor.author Drysdale, Jake
- dc.contributor.author Hockman, Jason
- dc.contributor.author Ramires, António
- dc.contributor.author Serra, Xavier
- dc.date.accessioned 2023-03-01T13:49:00Z
- dc.date.available 2023-03-01T13:49:00Z
- dc.date.issued 2022
- dc.description Comunicació presentada a 25th International Conference on Digital Audio Effects (DAFx20in22), celebrat del 6 al 10 de setembre de 2022 a Viena, Àustria.
- dc.description.abstract Many electronic music (EM) genres are composed through the activation of short audio recordings of instruments designed for seamless repetition—or loops. In this work, loops of key structural groups such as bass, percussive or melodic elements are labelled by the role they occupy in a piece of music through the task of automatic instrumentation role classification (AIRC). Such labels assist EM producers in the identification of compatible loops in large unstructured audio databases. While human annotation is often laborious, automatic classification allows for fast and scalable generation of these labels. We experiment with several deep-learning architectures and propose a data augmentation method for improving multi-label representation to balance classes within the Freesound Loop Dataset. To improve the classification accuracy of the architectures, we also evaluate different pooling operations. Results indicate that in combination with the data augmentation and pooling strategies, the proposed system achieves state-of-the-art performance for AIRC. Additionally, we demonstrate how our proposed AIRC method is useful for analysing the structure of EM compositions through loop activation transcription.
- dc.description.sponsorship This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 765068, MIP-Frontiers.
- dc.format.mimetype application/pdf
- dc.identifier.citation Drysdale J, Hockman J, Ramires A, Serra X. Improved automatic instrumentation role classification and loop activation transcription. In: Evangelista G, Holighaus N, editors. Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22); 2022 Sep 6-10; Vienna, Austria. [Vienna]: DAFx; 2022. p. 264-71.
- dc.identifier.issn 2413-6700
- dc.identifier.uri http://hdl.handle.net/10230/55991
- dc.language.iso eng
- dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
- dc.relation.ispartof Evangelista G, Holighaus N, editors. Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22); 2022 Sep 6-10; Vienna, Austria. [Vienna]: DAFx; 2022. p. 264-71.
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/765068
- dc.rights © 2022 Jake Drysdale et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, adaptation, and reproduction in any medium, provided the original author and source are credited.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.other Instrumentació i orquestració
- dc.subject.other Classificació automàtica
- dc.title Improved automatic instrumentation role classification and loop activation transcription
- dc.type info:eu-repo/semantics/conferenceObject
- dc.type.version info:eu-repo/semantics/publishedVersion