Romero, H.E., Ma, N. and Brown, G.J. orcid.org/0000-0001-8565-5476 (2020) Snorer diarisation based on deep neural network embeddings. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing, 04-08 May 2020, Barcelona, Spain (virtual conference). IEEE ISBN 9781509066322
Abstract
Acoustic analysis of sleep breathing sounds using a smartphone at home provides a much less obtrusive means of screening for sleep-disordered breathing (SDB) than assessment in a sleep clinic. However, application in a home environment is confounded by the problem that a bed partner may also be present and snore. This paper proposes a novel acoustic analysis system for snorer diarisation, a concept extrapolated from speaker diarisation research, which allows screening for SDB of both the user and the bed partner using a single smartphone. The snorer diarisation system involves three steps. First, a deep neural network (DNN) is employed to estimate the number of concurrent snorers in short segments of monaural audio recordings. Second, the identified snore segments are clustered using snorer embeddings, a feature representation that allows different snorers to be discriminated. Finally, a snore transcription is automatically generated for each snorer by combining consecutive snore segments. The system is evaluated on both synthetic snore mixtures and real two-snorer recordings. The results show that it is possible to accurately screen a subject and their bed partner for SDB in the same session from recordings of a single smartphone.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Snorer diarisation; sleep-disordered breathing; deep neural network embeddings; LSTM |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number INNOVATE UK (TSB) 26767 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Jun 2020 10:57 |
Last Modified: | 14 May 2021 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/icassp40776.2020.9053683 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161508 |