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A Bag of Wavelet Features for Snore Sound Classification

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Abstract

Snore sound (SnS) classification can support a targeted surgical approach to sleep related breathing disorders. Using machine listening methods, we aim to find the location of obstruction and vibration within a subject’s upper airway. Wavelet features have been demonstrated to be efficient in the recognition of SnSs in previous studies. In this work, we use a bag-of-audio-words approach to enhance the low-level wavelet features extracted from SnS data. A Naïve Bayes model was selected as the classifier based on its superiority in initial experiments. We use SnS data collected from 219 independent subjects under drug-induced sleep endoscopy performed at three medical centres. The unweighted average recall achieved by our proposed method is 69.4%, which significantly (\(p<0.005,\) one-tailed z-test) outperforms the official baseline (58.5%), and beats the winner (64.2%) of the INTERSPEECH ComParE Challenge 2017 Snoring sub-challenge. In addition, the conventionally used features like formants, mel-scale frequency cepstral coefficients, subband energy ratios, spectral frequency features, and the features extracted by the openSMILE toolkit are compared with our proposed feature set. The experimental results demonstrate the effectiveness of the proposed method in SnS classification.

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Acknowledgments

This work was partially supported by the China Scholarship Council (CSC), and the European Union’s Seventh Framework under Grant Agreements No. 338164 (ERC StG iHEARu).

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Authors and Affiliations

  1. Machine Intelligence & Signal Processing Group, MMK, Technische Universität München, Arcisstr. 21, 80333, Munich, Germany

    Kun Qian

  2. ZD.B Chair of Embedded Intelligence for Health Care & Wellbeing, Universität Augsburg, Eichleitnerstr. 30, 86159, Augsburg, Germany

    Kun Qian, Maximilian Schmitt & Björn Schuller

  3. Munich School of Bioengineering, Technische Universität München, Boltzmannstr. 11, 85748, Garching, Germany

    Christoph Janott & Werner Hemmert

  4. GLAM – Group on Language, Audio & Music, Department of Computing, Imperial College London, 180 Queens’ Gate, Huxley Bldg., London, SW7 2AZ, UK

    Zixing Zhang & Björn Schuller

  5. audEERING GmbH, 82206, Gilching, Germany

    Christoph Janott, Zixing Zhang & Björn Schuller

  6. Department of Otorhinolaryngology/Head and Neck Surgery, Klinikum rechts der Isar, Technische Universität München, Ismaningerstr. 22, 81675, Munich, Germany

    Clemens Heiser

  7. Department of Otorhinolaryngology/Head and Neck Surgery, Alfried Krupp Krankenhaus, Alfried-Krupp-Str. 21, 45131, Essen, Germany

    Winfried Hohenhorst

  8. Department of Otorhinolaryngology/Head and Neck Surgery, Carl-Thiem-Klinikum Cottbus, Thiemstr. 111, 03048, Cottbus, Germany

    Michael Herzog

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  1. Kun Qian
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Correspondence to Kun Qian.

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Associate Editor Ka-Wai Kwok oversaw the review of this article.

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Qian, K., Schmitt, M., Janott, C. et al. A Bag of Wavelet Features for Snore Sound Classification. Ann Biomed Eng 47, 1000–1011 (2019). https://doi.org/10.1007/s10439-019-02217-0

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