New parameters for respiratory sound classification

被引：0
|
作者：
Bahoura, M [1 ]
Pelletier, C [1 ]
机构：
[1] Univ Quebec, DMIG, Rimouski, PQ G5L 3A1, Canada
来源：
CCECE 2003: CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-3, PROCEEDINGS: TOWARD A CARING AND HUMANE TECHNOLOGY | 2003年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a new approach based on cepstral analysis is proposed to classify respiratory sounds. The sound signal is divided into segments, which are characterized by a reduced number of cepstral coefficients. Those segments are then classified as whether containing wheezes or normal respiratory sounds, by using the Vector Quantization (VQ) method. This approach is tested and compared to other kind of features extraction like the autoregressive representation and the wavelet transform.
引用
收藏
页码：1457 / 1460
页数：4
相关论文
共 50 条
  • [1] Wavelet packet based respiratory sound classification
    Pesu, L
    Ademovic, E
    Pesquet, JC
    Helisto, P
    PROCEEDINGS OF THE IEEE-SP INTERNATIONAL SYMPOSIUM ON TIME-FREQUENCY AND TIME-SCALE ANALYSIS, 1996, : 377 - 380
  • [2] Classification of Respiratory Conditions using Auscultation Sound
    Do, Quan T.
    Lipatov, Kirill
    Wang, Hsin-Yi
    Pickering, Brian W.
    Herasevich, Vitaly
    2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), 2021, : 1942 - 1945
  • [3] Snoring Sound Classification from Respiratory Signal
    Shokrollahi, Mehrnaz
    Saha, Shumit
    Hadi, Peyman
    Rudzicz, Frank
    Yadollahi, Azadeh
    2016 38TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2016, : 3215 - 3218
  • [4] New approaches for spectro-temporal feature extraction with applications to respiratory sound classification
    Jin, F.
    Sattar, F.
    Goh, D. Y. T.
    NEUROCOMPUTING, 2014, 123 : 362 - 371
  • [5] Fabric-sound classification by autoregressive parameters
    Yi, E
    Cho, G
    JOURNAL OF THE TEXTILE INSTITUTE, 2000, 91 (04) : 530 - 545
  • [6] Classification of Adventitious Respiratory Sound Events: A Stratified Analysis
    Fernandes, Tiago
    Rocha, Bruno M.
    Pessoa, Diogo
    de Carvalho, Paulo
    Paiva, Rui Pedro
    2022 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) JOINTLY ORGANISED WITH THE IEEE-EMBS INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN'22), 2022,
  • [7] A GLANCE-AND-GAZE NETWORK FOR RESPIRATORY SOUND CLASSIFICATION
    Yu, Shuai
    Ding, Yiwei
    Qian, Kun
    Hu, Bin
    Li, Wei
    Schuller, Bjoern W.
    2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, : 9007 - 9011
  • [8] Feature Extraction and Classification of Respiratory Sound and Lung Diseases
    Latifi, Seyed Amir
    Ghassemian, Hassan
    Imani, Maryam
    2023 6TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION AND IMAGE ANALYSIS, IPRIA, 2023,
  • [9] CONTRASTIVE EMBEDDIND LEARNING METHOD FOR RESPIRATORY SOUND CLASSIFICATION
    Song, Wenjie
    Han, Jiqing
    Song, Hongwei
    2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, : 1275 - 1279
  • [10] Automatic Classification of Respiratory Sound Considering Hierarchical Structure
    Tabata, Manami
    Lu, Huimin
    Kamiya, Tohru
    Mabu, Shingo
    Kido, Shoji
    2022 22ND INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2022), 2022, : 537 - 541
12345