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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/75044

Title:	Classification of non-speech human sounds: Feature selection and snoring sound analysis
Authors:	Liao, Wen-Hung;Lin, Y.-K. 廖文宏
Contributors:	資訊科學系
Keywords:	Acoustic features;Audio classification;Audio signal;Environmental sounds;Extensive simulations;Feature selection;Human sounds;Human speech;Multivariate adaptive regression splines;Obstructive sleep apnea;Scene analysis;Sound analysis;Classification (of information);Cybernetics;Sleep research;Support vector machines;Audio acoustics
Date:	2009
Issue Date:	2015-05-07 17:40:17 (UTC+8)
Abstract:	Human sounds can be roughly divided into two categories: speech and non-speech. Traditional audio scene analysis research puts more emphasis on the classification of audio signals into human speech, music, and environmental sounds. We take a different perspective in this paper. We are mainly interested in the analysis of non-speech human sounds, including laugh, scream, sneeze, and snore. Toward this goal, we investigate many commonly used acoustic features and select useful ones for classification using multivariate adaptive regression splines (MARS) and support vector machine (SVM). To evaluate the robustness of the selected features, we also perform extensive simulations to observe the effect of noise on the accuracy of the classification. Finally, for the class of snoring sounds, we propose a robust approach to further categorize them into simple snores and snores of subjects with obstructive sleep apnea (OSA). ©2009 IEEE.
Relation:	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics,2695-2700
Data Type:	conference
DOI link:	http://dx.doi.org/10.1109/ICSMC.2009.5346556
DOI:	10.1109/ICSMC.2009.5346556
Appears in Collections:	[Department of Computer Science ] Proceedings

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