Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation
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 feat...
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| Format: | Article |
| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2018-07-01
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| Series: | Archives of Acoustics |
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| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/2155 |
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| _version_ | 1849705442472624128 |
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| author | Kun QIAN Christoph JANOTT Zixing ZHANG Jun DENG Alice BAIRD Clemens HEISER Winfried HOHENHORST Michael HERZOG Werner HEMMERT Björn SCHULLER |
| author_facet | Kun QIAN Christoph JANOTT Zixing ZHANG Jun DENG Alice BAIRD Clemens HEISER Winfried HOHENHORST Michael HERZOG Werner HEMMERT Björn SCHULLER |
| author_sort | Kun QIAN |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0ffbbd0c10f8431e92e235087780a592 |
| institution | DOAJ |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2018-07-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-0ffbbd0c10f8431e92e235087780a5922025-08-20T03:16:28ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2018-07-0143310.24425/123918Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation LocalisationKun QIAN0Christoph JANOTT1Zixing ZHANG2Jun DENG3Alice BAIRD4Clemens HEISER5Winfried HOHENHORST6Michael HERZOG7Werner HEMMERT8Björn SCHULLER9Technical University of Munich, University of PassauTechnical University of MunichUniversity of PassauaudEERING GmbHUniversity of PassauTechnical University of MunichClinic for ENT Medicine, Head and Neck Surgery, Alfried Krupp Krankenhaus, Essen, GermanyClinic for ENT Medicine, Head and Neck Surgery, Cottbus, GermanyTechnical University of MunichUniversity of Passau, Imperial College London, audEERING GmbHThis 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.https://acoustics.ippt.pan.pl/index.php/aa/article/view/2155snore soundobstructive sleep apneaacoustic featuresmachine learning |
| spellingShingle | Kun QIAN Christoph JANOTT Zixing ZHANG Jun DENG Alice BAIRD Clemens HEISER Winfried HOHENHORST Michael HERZOG Werner HEMMERT Björn SCHULLER Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation Archives of Acoustics snore sound obstructive sleep apnea acoustic features machine learning |
| title | Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation |
| title_full | Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation |
| title_fullStr | Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation |
| title_full_unstemmed | Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation |
| title_short | Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation |
| title_sort | teaching machines on snoring a benchmark on computer audition for snore sound excitation localisation |
| topic | snore sound obstructive sleep apnea acoustic features machine learning |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/2155 |
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