Browse > Article
http://dx.doi.org/10.9723/jksiis.2018.23.2.011

Snoring Sound Classification using Efficient Spectral Features and SVM for Smart Pillow  

Kim, Byeong Man (금오공과대학교 컴퓨터소프트웨어공학과)
Moon, Chang Bae (금오공과대학교 ICT융합특성화연구센터)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.23, no.2, 2018 , pp. 11-18 More about this Journal
Abstract
Severe snoring can lead to OSA(Obstructive Sleep Apnea), which can lead to life-threatening cases, and snoring can lead to serious pernicious relationships. In order to solve these snoring problems, several types of smart pillows have recently been released. The core technology is snoring discrimination technology, ie, a technique for determining whether snoring is included in the input sound. In this paper, we propose a snoring detection method to apply to a smart pillow. After extracting the features of the snoring sound from the input signal, we discriminate the snoring using these features and SVM. In order to measure the performance of the proposed method, comparative experiments with the existing methods are performed. The experimental results show about 6% better discrimination performance than the existing method.
Keywords
Smart Pillow; SVM; Snoring Sound Classification; Spectral Features;
Citations & Related Records
연도 인용수 순위
  • Reference
1 T Young, L Finn, PE Peppard, et al, "Sleep Disordered Breathing and Mortality: Eighteen-Year Follow-up of the Wisconsin Sleep Cohort," Sleep, Vol. 31 No. 8, pp. 1071-1078, 2008.
2 C Zamarron, F Gude, YO Otero, JR Rodriguez-Suarez, "Snoring and Myocardial Infarction: a 4-Year Follow-up Study," Respir, Med, 93, pp. 108-12, 1999.   DOI
3 J. Zhang, Q. Zhang, Y. Wang, and C. Qiu, "A Real-time Autoadjustable Smart Pillow System for Sleep Apnea Detection and Treatment," in Proc. 12th Int. Conf. Inf. Process. Sens. Netw., pp. 179-190, 2013.
4 W. Gu, L. Shangguan, Z. Yang, and Y. Liu, "Sleep Hunter: Towards Fine Grained Sleep Stage Tracking with Smartphones," IEEE Transactions on Mobile Computing, Vol. 15, No. 6, pp. 1514-1527, 2016.   DOI
5 R. Wei, Kim, H.S. X. Li, Im, J.J. and Kim, H.J., "A Development of Pillow for Detection and Restraining of Snoring," In Biomedical Engineering and Informatics(BMEI), IEEE, 2010 3rd International Conference on, Vol. 4, pp. 1381-1385, 2010.
6 Sun, X., Kim, J.Y. Won, Y., et al, "Efficient Snoring and Breathing Detection Based on Sub-Band Spectral Statistics," Bio-Medical Materials and Engineering, 26(s1), pp. 787-793, 2015.   DOI
7 Chang, C.C. Lin, C.J., "LIBSVM: A Library for Support Vector Machines," ACM Transactions on Intelligent Systems and Technology, Vol. 2, No. 27, 2011.
8 M, Frigo., Johnson, S.G., "FFTW: An Adaptive Software Architecture for the FFT," Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. Vol. 3, pp. 1381-1384, 1998.
9 Moon, C.B. Kim, H.S. Kim, B.M., "Audio Recorder Identification using Reduced Noise Features," Ubiquitous Information Technologies and Applications, Springer, pp. 35-42, 2014.
10 Sun, X., Kim, J.Y. and Won, Y.G., "Simple and Efficient Spectral Features for Breathing and Snoring Sound Classification," Journal of Korean Institute of Information Technology Vol. 12, No. 12, pp. 69-75, 2014.
11 Beck, R., Odeh, M., Oliven, A., and Gavriely, N., "The Acoustic Properties of Snores," European Respiratory Journal, Vol. 8, No. 12, pp. 2120-2128, 1995.   DOI