An 'End-to-evolution' hybrid approach for snore sound classification

Michael Freitag, Shahin Amiriparian, Nicholas Cummins, Maurice Gerczuk, Björn Schuller

Research output: Contribution to journalConference articlepeer-review

28 Scopus citations

Abstract

Whilst snoring itself is usually not harmful to a person's health, it can be an indication of Obstructive Sleep Apnoea (OSA), a serious sleep-related disorder. As a result, studies into using snoring as acoustic based marker of OSA are gaining in popularity. Motivated by this, the INTERSPEECH 2017 ComParE Snoring sub-challenge requires classification from which areas in the upper airways different snoring sounds originate. This paper explores a hybrid approach combining evolutionary feature selection based on competitive swarm optimisation and deep convolutional neural networks (CNN). Feature selection is applied to novel deep spectrum features extracted directly from spectrograms using pre-trained image classification CNN. Key results presented demonstrate that our hybrid approach can substantially increase the performance of a linear support vector machine on a set of low-level features extracted from the Snoring sub-challenge data. Even without subset selection, the deep spectrum features are sufficient to outperform the challenge baseline, and competitive swarm optimisation further improves system performance. In comparison to the challenge baseline, unweighted average recall is increased from 40.6 % to 57.6 % on the development partition, and from 58.5 % to 66.5 % on the test partition, using 2 246 of the 4 096 deep spectrum features.

Original languageEnglish
Pages (from-to)3507-3511
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2017-August
DOIs
StatePublished - 2017
Externally publishedYes
Event18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017

Keywords

  • Competitive swarm optimisation
  • Computational paralinguistics
  • Convolutional neural network
  • Evolutionary feature selection
  • Snoring

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