Archives of Acoustics, 43, 3, pp. 465–475, 2018
10.24425/123918

Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation

Kun QIAN
Technical University of Munich, University of Passau
Germany

Christoph JANOTT
Technical University of Munich
Germany

Zixing ZHANG
University of Passau
Germany

Jun DENG
audEERING GmbH
Germany

Alice BAIRD
University of Passau
Germany

Clemens HEISER
Technical University of Munich
Germany

Winfried HOHENHORST
Clinic for ENT Medicine, Head and Neck Surgery, Alfried Krupp Krankenhaus, Essen, Germany
Germany

Michael HERZOG
Clinic for ENT Medicine, Head and Neck Surgery, Cottbus, Germany
Germany

Werner HEMMERT
Technical University of Munich
Germany

Björn SCHULLER
University of Passau, Imperial College London, audEERING GmbH
Germany

This paper proposes a comprehensive study on machine listening for localisation of snore sound excitation. Here we investigate the effects of varied frame sizes, and overlap of the analysed audio chunk for extracting low-level descriptors. In addition, we explore the performance of each kind of feature when it is fed into varied classifier models, including support vector machines, $k$-nearest neighbours, linear discriminant analysis, random forests, extreme learning machines, kernel-based extreme learning machines, multilayer perceptrons, and deep neural networks. Experimental results demonstrate that, wavelet packet transform energy can outperform most other features. A deep neural network trained with subband energy ratios reaches the highest performance achieving an unweighted average recall of 72.8% from four types for snoring.
Keywords: snore sound; obstructive sleep apnea; acoustic features; machine learning
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DOI: 10.24425/123918

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