Opin vísindi

Speed-Up of Machine Learning for Sound Localization via High-Performance Computing

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Sumner, Eric Michael
dc.contributor.author Aach, Marcel
dc.contributor.author Lintermann, Andreas
dc.contributor.author Unnthorsson, Runar
dc.contributor.author Riedel, Morris
dc.date.accessioned 2022-07-28T08:13:53Z
dc.date.available 2022-07-28T08:13:53Z
dc.date.issued 2022-02-16
dc.identifier.citation E. M. Sumner, M. Aach, A. Lintermann, R. Unnthorsson and M. Riedel, "Speed-Up of Machine Learning for Sound Localization via High-Performance Computing," 2022 26th International Conference on Information Technology (IT), 2022, pp. 1-4, doi: 10.1109/IT54280.2022.9743519.
dc.identifier.isbn 978-1-6654-2127-0
dc.identifier.uri https://hdl.handle.net/20.500.11815/3304
dc.description Post-print (lokaútgáfa höfunda).
dc.description.abstract Sound localization is the ability of humans to determine the source direction of sounds that they hear. Emulating this capability in virtual environments can have various societally relevant applications enabling more realistic virtual acoustics. We use a variety of artificial intelligence methods, such as machine learning via an Artificial Neural Network (ANN) model, to emulate human sound localization abilities. This paper addresses the particular challenge that the training and optimization of these models is very computationally-intensive when working with audio signal datasets. It describes the successful porting of our novel ANN model code for sound localization from limiting serial CPU-based systems to powerful, cutting-edge High-Performance Computing (HPC) resources to obtain significant speed-ups of the training and optimization process. Selected details of the code refactoring and HPC porting are described, such as adapting hyperparameter optimization algorithms to efficiently use the available HPC resources and replacing third-party libraries responsible for audio signal analysis and linear algebra. This study demonstrates that using innovative HPC systems at the Jülich Supercomputing Centre, equipped with high-tech Graphics Processing Unit (GPU) resources and based on the Modular Supercomputing Architecture, enables significant speed-ups and reduces the time-to-solution for sound localization from three days to three hours per ANN model.
dc.format.extent 1-4
dc.language.iso en
dc.publisher IEEE
dc.relation info:eu-repo/grantAgreement/EC/H2020/951733
dc.relation.ispartofseries 2022 26th International Conference on Information Technology (IT);
dc.rights info:eu-repo/semantics/openAccess
dc.subject Reiknilíkön
dc.subject Hugbúnaðargerð
dc.subject Gervigreind
dc.title Speed-Up of Machine Learning for Sound Localization via High-Performance Computing
dc.type info:eu-repo/semantics/conferenceObject
dcterms.license © 2022 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.
dc.description.version Peer Reviewed
dc.identifier.journal 2022 26th International Conference on Information Technology (IT)
dc.identifier.doi 10.1109/IT54280.2022.9743519
dc.contributor.department Iðnaðarverkfræði-, vélaverkfræði- og tölvunarfræðideild (HÍ)
dc.contributor.department Faculty of Industrial Eng., Mechanical Eng. and Computer Science (UI)
dc.contributor.school Verkfræði- og náttúruvísindasvið (HÍ)
dc.contributor.school School of Engineering and Natural Sciences (UI)

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