Feature Analysis of Snore Signals and Other Sound Signals Based on Complex Order Derivative Processing

Jiangbo Zhao; Xiaodong Wang; Junzheng Wang

doi:10.1109/CCDC52312.2021.9601605

Feature Analysis of Snore Signals and Other Sound Signals Based on Complex Order Derivative Processing

Jiangbo Zhao, Xiaodong Wang^*, Junzheng Wang

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

The key to snore recognition is to process snore signals and acquire snore features. There is often interference from other sounds during sleep, such as cough sounds and tapping sounds. The snore features extracted by the existing snore processing methods are not significantly different from the environmental noise during sleep, which leads to the complicated algorithm and low accuracy of snore recognition when there is environmental noise interference. In this paper, the complex order derivative was used to process snore signals and extract snore features. The experimental results showed that the snore signals processed by the complex order derivative were obviously different from the ambient noise, and it can be applied to snore recognition.

Original language	English
Title of host publication	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2013-2017
Number of pages	5
ISBN (Electronic)	9781665440899
DOIs	https://doi.org/10.1109/CCDC52312.2021.9601605
Publication status	Published - 2021
Event	33rd Chinese Control and Decision Conference, CCDC 2021 - Kunming, China Duration: 22 May 2021 → 24 May 2021

Publication series

Name	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

Conference

Conference	33rd Chinese Control and Decision Conference, CCDC 2021
Country/Territory	China
City	Kunming
Period	22/05/21 → 24/05/21

Keywords

Complex Order Derivative
Feature Extraction
Snore Signal Processing
Snoring

Access to Document

10.1109/CCDC52312.2021.9601605

Cite this

Zhao, J., Wang, X., & Wang, J. (2021). Feature Analysis of Snore Signals and Other Sound Signals Based on Complex Order Derivative Processing. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021 (pp. 2013-2017). (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC52312.2021.9601605

Zhao, Jiangbo ; Wang, Xiaodong ; Wang, Junzheng. / Feature Analysis of Snore Signals and Other Sound Signals Based on Complex Order Derivative Processing. Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 2013-2017 (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021).

@inproceedings{9a3c6612422b4715be4d23afa139f212,

title = "Feature Analysis of Snore Signals and Other Sound Signals Based on Complex Order Derivative Processing",

abstract = "The key to snore recognition is to process snore signals and acquire snore features. There is often interference from other sounds during sleep, such as cough sounds and tapping sounds. The snore features extracted by the existing snore processing methods are not significantly different from the environmental noise during sleep, which leads to the complicated algorithm and low accuracy of snore recognition when there is environmental noise interference. In this paper, the complex order derivative was used to process snore signals and extract snore features. The experimental results showed that the snore signals processed by the complex order derivative were obviously different from the ambient noise, and it can be applied to snore recognition.",

keywords = "Complex Order Derivative, Feature Extraction, Snore Signal Processing, Snoring",

author = "Jiangbo Zhao and Xiaodong Wang and Junzheng Wang",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd Chinese Control and Decision Conference, CCDC 2021 ; Conference date: 22-05-2021 Through 24-05-2021",

year = "2021",

doi = "10.1109/CCDC52312.2021.9601605",

language = "English",

series = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",

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

pages = "2013--2017",

booktitle = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",

address = "United States",

}

Zhao, J, Wang, X & Wang, J 2021, Feature Analysis of Snore Signals and Other Sound Signals Based on Complex Order Derivative Processing. in Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 2013-2017, 33rd Chinese Control and Decision Conference, CCDC 2021, Kunming, China, 22/05/21. https://doi.org/10.1109/CCDC52312.2021.9601605

Feature Analysis of Snore Signals and Other Sound Signals Based on Complex Order Derivative Processing. / Zhao, Jiangbo; Wang, Xiaodong; Wang, Junzheng.
Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2013-2017 (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Feature Analysis of Snore Signals and Other Sound Signals Based on Complex Order Derivative Processing

AU - Zhao, Jiangbo

AU - Wang, Xiaodong

AU - Wang, Junzheng

PY - 2021

Y1 - 2021

N2 - The key to snore recognition is to process snore signals and acquire snore features. There is often interference from other sounds during sleep, such as cough sounds and tapping sounds. The snore features extracted by the existing snore processing methods are not significantly different from the environmental noise during sleep, which leads to the complicated algorithm and low accuracy of snore recognition when there is environmental noise interference. In this paper, the complex order derivative was used to process snore signals and extract snore features. The experimental results showed that the snore signals processed by the complex order derivative were obviously different from the ambient noise, and it can be applied to snore recognition.

AB - The key to snore recognition is to process snore signals and acquire snore features. There is often interference from other sounds during sleep, such as cough sounds and tapping sounds. The snore features extracted by the existing snore processing methods are not significantly different from the environmental noise during sleep, which leads to the complicated algorithm and low accuracy of snore recognition when there is environmental noise interference. In this paper, the complex order derivative was used to process snore signals and extract snore features. The experimental results showed that the snore signals processed by the complex order derivative were obviously different from the ambient noise, and it can be applied to snore recognition.

KW - Complex Order Derivative

KW - Feature Extraction

KW - Snore Signal Processing

KW - Snoring

UR - http://www.scopus.com/inward/record.url?scp=85125169181&partnerID=8YFLogxK

U2 - 10.1109/CCDC52312.2021.9601605

DO - 10.1109/CCDC52312.2021.9601605

M3 - Conference contribution

AN - SCOPUS:85125169181

T3 - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

SP - 2013

EP - 2017

BT - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 33rd Chinese Control and Decision Conference, CCDC 2021

Y2 - 22 May 2021 through 24 May 2021

ER -

Zhao J, Wang X, Wang J. Feature Analysis of Snore Signals and Other Sound Signals Based on Complex Order Derivative Processing. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2013-2017. (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). doi: 10.1109/CCDC52312.2021.9601605

Feature Analysis of Snore Signals and Other Sound Signals Based on Complex Order Derivative Processing

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this