Data augmentation for instrument classification robust to audio effects

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• dc.contributor.author Ramires, António
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2019-08-01T07:30:50Z
• dc.date.available 2019-08-01T07:30:50Z
• dc.date.issued 2019
• dc.description Comunicació presentada a la 22a International Conference on Digital Audio Effects (DAFx-19) que se celebra del 2 al 6 de setembre de 2019 a Birmingham, Regne Unit.
• dc.description.abstract Reusing recorded sounds (sampling) is a key component in Electronic Music Production (EMP), which has been present since its early days and is at the core of genres like hip-hop or jungle. Commercial and non-commercial services allow users to obtain collections of sounds (sample packs) to reuse in their compositions. Automatic classification of one-shot instrumental sounds allows automatically categorising the sounds contained in these collections, allowing easier navigation and better characterisation. Automatic instrument classification has mostly targeted the classification of unprocessed isolated instrumental sounds or detecting predominant instruments in mixed music tracks. For this classification to be useful in audio databases for EMP, it has to be robust to the audio effects applied to unprocessed sounds. In this paper we evaluate how a state of the art model trained with a large dataset of one-shot instrumental sounds performs when classifying instruments processed with audio effects. In order to evaluate the robustness of the model, we use data augmentation with audio effects and evaluate how each effect influences the classification accuracy.
• 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. We thank Matthew Davies for reviewing a draft of this paper and providing helpful feedback.
• dc.format.mimetype application/pdf
• dc.identifier.citation Ramires A, Serra X. Data augmentation for instrument classification robust to audio effects. Paper presented at: 22nd International Conference on Digital Audio Effects (DAFx-19); 2019 Sep 2-6; Birmingham, UK.
• dc.identifier.uri http://hdl.handle.net/10230/42218
• dc.language.iso eng
• dc.publisher DAFx
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/765068
• dc.rights © 2019 António Ramires 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 Data augmentation for instrument classification robust to audio effects
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion