Romero, H., Ma, N., Brown, G. orcid.org/0000-0001-8565-5476 et al. (2 more authors) (2019) Deep learning features for robust detection of acoustic events in sleep-disordered breathing. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2019). IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12-17 May 2019, Brighton, UK. IEEE ISBN 9781479981311
Abstract
Sleep-disordered breathing (SDB) is a serious and prevalent condition, and acoustic analysis via consumer devices (e.g. smartphones) offers a low-cost solution to screening for it. We present a novel approach for the acoustic identification of SDB sounds, such as snoring, using bottleneck features learned from a corpus of whole-night sound recordings. Two types of bottleneck features are described, obtained by applying a deep autoencoder to the output of an auditory model or a short-term autocorrelation analysis. We investigate two architectures for snore sound detection: a tandem system and a hybrid system. In both cases, a `language model' (LM) was incorporated to exploit information about the sequence of different SDB events. Our results show that the proposed bottleneck features give better performance than conventional mel-frequency cepstral coefficients, and that the tandem system outperforms the hybrid system given the limited amount of labelled training data available. The LM made a small improvement to the performance of both classifiers.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 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: | Sleep-disordered breathing; deep learning; hidden Markov model; bottleneck features; corpus |
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) KTP009905 PASSION FOR LIFE HEALTHCARE (UK) LIMITED C002 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Feb 2019 13:37 |
Last Modified: | 17 Apr 2020 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICASSP.2019.8683099 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142475 |