Automatic detection of inspiration related snoring signals from original audio recording

Kun Qian; Zhiyong Xu; Huijie Xu; Boon Poh Ng

doi:10.1109/ChinaSIP.2014.6889209

Automatic detection of inspiration related snoring signals from original audio recording

Kun Qian, Zhiyong Xu, Huijie Xu, Boon Poh Ng

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

13 引用（Scopus）

摘要

Inspiration related snoring signals (IRSS) are essential for doctors and researchers to develop further study and establishment of personal health database. How to detect IRSS automatically from original audio recording is significant in methods of acoustic based Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) diagnosis and monitoring. We proposed a systematic approach combining signal processing with machine learning techniques to detect IRSS from audio recording. Both the experimental results and computer studies demonstrate the efficiency of the proposed approach.

源语言	英语
主期刊名	2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings
出版商	Institute of Electrical and Electronics Engineers Inc.
页	95-99
页数	5
ISBN（电子版）	9781479954032
DOI	https://doi.org/10.1109/ChinaSIP.2014.6889209
出版状态	已出版 - 3 9月 2014
已对外发布	是
活动	2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Xi'an, 中国期限: 9 7月 2014 → 13 7月 2014

出版系列

姓名	2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings

会议

会议	2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014
国家/地区	中国
市	Xi'an
时期	9/07/14 → 13/07/14

访问文件

10.1109/ChinaSIP.2014.6889209

其它文件与链接

链接到 Scopus 的出版物

引用此

Qian, K., Xu, Z., Xu, H., & Ng, B. P. (2014). Automatic detection of inspiration related snoring signals from original audio recording. 在 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings (页码 95-99). 文章 6889209 (2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ChinaSIP.2014.6889209

Qian, Kun ; Xu, Zhiyong ; Xu, Huijie 等. / Automatic detection of inspiration related snoring signals from original audio recording. 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. 页码 95-99 (2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings).

@inproceedings{39812d84743d462f9bc79b53f0146249,

title = "Automatic detection of inspiration related snoring signals from original audio recording",

abstract = "Inspiration related snoring signals (IRSS) are essential for doctors and researchers to develop further study and establishment of personal health database. How to detect IRSS automatically from original audio recording is significant in methods of acoustic based Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) diagnosis and monitoring. We proposed a systematic approach combining signal processing with machine learning techniques to detect IRSS from audio recording. Both the experimental results and computer studies demonstrate the efficiency of the proposed approach.",

keywords = "Apnea/Hypopnea Syndrome, Obstructive Sleep, inspiration related snoring signals, machine learning, signal processing",

author = "Kun Qian and Zhiyong Xu and Huijie Xu and Ng, {Boon Poh}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 ; Conference date: 09-07-2014 Through 13-07-2014",

year = "2014",

month = sep,

day = "3",

doi = "10.1109/ChinaSIP.2014.6889209",

language = "English",

series = "2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "95--99",

booktitle = "2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings",

address = "United States",

}

Qian, K, Xu, Z, Xu, H & Ng, BP 2014, Automatic detection of inspiration related snoring signals from original audio recording. 在 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings., 6889209, 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 页码 95-99, 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014, Xi'an, 中国, 9/07/14. https://doi.org/10.1109/ChinaSIP.2014.6889209

Automatic detection of inspiration related snoring signals from original audio recording. / Qian, Kun; Xu, Zhiyong; Xu, Huijie 等.
2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. 页码 95-99 6889209 (2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Automatic detection of inspiration related snoring signals from original audio recording

AU - Qian, Kun

AU - Xu, Zhiyong

AU - Xu, Huijie

AU - Ng, Boon Poh

PY - 2014/9/3

Y1 - 2014/9/3

N2 - Inspiration related snoring signals (IRSS) are essential for doctors and researchers to develop further study and establishment of personal health database. How to detect IRSS automatically from original audio recording is significant in methods of acoustic based Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) diagnosis and monitoring. We proposed a systematic approach combining signal processing with machine learning techniques to detect IRSS from audio recording. Both the experimental results and computer studies demonstrate the efficiency of the proposed approach.

AB - Inspiration related snoring signals (IRSS) are essential for doctors and researchers to develop further study and establishment of personal health database. How to detect IRSS automatically from original audio recording is significant in methods of acoustic based Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) diagnosis and monitoring. We proposed a systematic approach combining signal processing with machine learning techniques to detect IRSS from audio recording. Both the experimental results and computer studies demonstrate the efficiency of the proposed approach.

KW - Apnea/Hypopnea Syndrome

KW - Obstructive Sleep

KW - inspiration related snoring signals

KW - machine learning

KW - signal processing

UR - http://www.scopus.com/inward/record.url?scp=84929408967&partnerID=8YFLogxK

U2 - 10.1109/ChinaSIP.2014.6889209

DO - 10.1109/ChinaSIP.2014.6889209

M3 - Conference contribution

AN - SCOPUS:84929408967

T3 - 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings

SP - 95

EP - 99

BT - 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014

Y2 - 9 July 2014 through 13 July 2014

ER -

Qian K, Xu Z, Xu H, Ng BP. Automatic detection of inspiration related snoring signals from original audio recording. 在 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. 页码 95-99. 6889209. (2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings). doi: 10.1109/ChinaSIP.2014.6889209

Automatic detection of inspiration related snoring signals from original audio recording

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