Feature Extraction for Cough-sound Recognition Based on Principle Component Analysis and Non-uniform Filter-bank_Journal of Biomedical Engineering

Authors：

ZHUChunmei ^1,2 ,  MOHongqiang ² , TIANLianfang ² , ZHENGZeguang ³

1. Mechanical & Electrical Engineering Department, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528403, China;
2. College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China;
3. First Affiliated Hospital of Guangzhou Medical College, Guangzhou 510120, China;

Corresponding author：

MOHongqiang, Email: hqiangmo@scut.edu.cn

Keywords：

cough; feature extraction; principle component analysis; filter-bank

DOI：

10.7507/1001-5515.20150136

Video：

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Abstract Full text Figures/Tables Video References Cited by

Cough recognition provides important clinical information for the treatment of many respiratory diseases. A new Mel frequency cepstrum coefficient (MFCC) extracting method has been proposed on the basis of the distributional characteristics of cough spectrum. The whole frequency band was divided into several sub-bands, and the energy coefficient for each band was obtained by method of principle component analysis. Then non-uniform filter-bank in Mel frequency is designed to improve the extracting process of MFCC by distributing filters according to the spectrum energy coefficients. Cough recognition experiment using hidden Markov model was carried out, and the results showed that the proposed method could effectively improve the performance of cough recognition.

Citation： ZHUChunmei, MOHongqiang, TIANLianfang, ZHENGZeguang. Feature Extraction for Cough-sound Recognition Based on Principle Component Analysis and Non-uniform Filter-bank. Journal of Biomedical Engineering, 2015, 32(4): 746-750. doi: 10.7507/1001-5515.20150136 Copy

1.	HSU J Y,STONE R A,LOGAN-SINCLAIR R B,et al.Coughing frequency in patients with persistent cough:assessment using a 24 hour ambulatory recorder[J].Eur Respir J,1994,7(7):1246-1253.
2.	SUBBURAJ S,PARVEZ L,RAJAGOPALAN T G.Methods of recording and analyzing cough sounds[J].Pulm Pharmacol,1996,9(5-6):269-279.
3.	CHANG A B,NEWMAN R G,PHELAN P D,et al.A new use for an old Holter monitor:an ambulatory cough meter[J].Eur Respir J,1997,10(7):1637-1639.
4.	MATOS S,BIRRING S S,PAVORD I D,et al.Detection of cough signals in continuous audio recordings using hidden Markov models[J].IEEE Trans Biomed Eng,2006,53(6):1078-1083.
5.	MATOS S,BIRRING S S,PAVORD I D,et al.An automated system for 24-h monitoring of cough frequency:the leicester cough monitor[J].IEEE Trans Biomed Eng,2007,54(8):1472-1479.
6.	SHIN S H,HASHIMOTO T,HATANO S.Automatic detection system for cough sounds as a symptom of abnormal health condition[J].IEEE Trans Inf Technol Biomed,2009,13(4):486-493.
7.	BARRY S J,DANE A D,MORICE A H,et al.The automatic recognition and counting of cough[J].Cough,2006,2(1):8.
8.	石锐,王博,何庆华.基于高斯混合模型的咳嗽音检测方法[J].计算机工程与应用,2011,47(32):151-154.
9.	PICONE J W.Signal modeling techniques in speech recognition[J].Proceedings of the IEEE,1993,81(9):1215-1247.
10.	ZHU Chunmei,TIAN Lianfang,LI Xiangyang,et al.Recognition of cough using features improved by sub-band energy transformation[C]//BMEI 2013:6th International Conference on Biomedical Engineering and Informatics.Hangzhou,China:2013,2013:251-255.
11.	KORPÁŠ J,SADLOÑOVÁ J,VRABEC M.Analysis of the cough sound:an overview[J].Pulm Pharmacol,1996,9(5-6):261-268.
12.	VAN HIRTUM A,BERCKMANS D.Assessing the sound of cough towards vocality[J].Med Eng Phys,2002,24(7/8):535-540.
13.	ABDI H,WILLIAMS L J.Principal component analysis[J].Wiley Interdiscip Rev Comput Stat,2010,2(4):433-459.
14.	RABINER L R.A tutorial on hidden Markov models and selected applications in speech recognition[J].Proceedings of the IEEE,1989,77(2):257-286.

1. HSU J Y,STONE R A,LOGAN-SINCLAIR R B,et al.Coughing frequency in patients with persistent cough:assessment using a 24 hour ambulatory recorder[J].Eur Respir J,1994,7(7):1246-1253.
2. SUBBURAJ S,PARVEZ L,RAJAGOPALAN T G.Methods of recording and analyzing cough sounds[J].Pulm Pharmacol,1996,9(5-6):269-279.
3. CHANG A B,NEWMAN R G,PHELAN P D,et al.A new use for an old Holter monitor:an ambulatory cough meter[J].Eur Respir J,1997,10(7):1637-1639.
4. MATOS S,BIRRING S S,PAVORD I D,et al.Detection of cough signals in continuous audio recordings using hidden Markov models[J].IEEE Trans Biomed Eng,2006,53(6):1078-1083.
5. MATOS S,BIRRING S S,PAVORD I D,et al.An automated system for 24-h monitoring of cough frequency:the leicester cough monitor[J].IEEE Trans Biomed Eng,2007,54(8):1472-1479.
6. SHIN S H,HASHIMOTO T,HATANO S.Automatic detection system for cough sounds as a symptom of abnormal health condition[J].IEEE Trans Inf Technol Biomed,2009,13(4):486-493.
7. BARRY S J,DANE A D,MORICE A H,et al.The automatic recognition and counting of cough[J].Cough,2006,2(1):8.
8. 石锐,王博,何庆华.基于高斯混合模型的咳嗽音检测方法[J].计算机工程与应用,2011,47(32):151-154.
9. PICONE J W.Signal modeling techniques in speech recognition[J].Proceedings of the IEEE,1993,81(9):1215-1247.
10. ZHU Chunmei,TIAN Lianfang,LI Xiangyang,et al.Recognition of cough using features improved by sub-band energy transformation[C]//BMEI 2013:6th International Conference on Biomedical Engineering and Informatics.Hangzhou,China:2013,2013:251-255.
11. KORPÁŠ J,SADLOÑOVÁ J,VRABEC M.Analysis of the cough sound:an overview[J].Pulm Pharmacol,1996,9(5-6):261-268.
12. VAN HIRTUM A,BERCKMANS D.Assessing the sound of cough towards vocality[J].Med Eng Phys,2002,24(7/8):535-540.
13. ABDI H,WILLIAMS L J.Principal component analysis[J].Wiley Interdiscip Rev Comput Stat,2010,2(4):433-459.
14. RABINER L R.A tutorial on hidden Markov models and selected applications in speech recognition[J].Proceedings of the IEEE,1989,77(2):257-286.

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