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題名 Classification of non-speech human sounds: Feature selection and snoring sound analysis
作者 Liao, Wen-Hung;Lin, Y.-K.
廖文宏
貢獻者 資訊科學系
關鍵詞 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
日期 2009
上傳時間 7-May-2015 17:40:17 (UTC+8)
摘要 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.
關聯 Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics,2695-2700
資料類型 conference
DOI http://dx.doi.org/10.1109/ICSMC.2009.5346556
dc.contributor 資訊科學系
dc.creator (作者) Liao, Wen-Hung;Lin, Y.-K.
dc.creator (作者) 廖文宏zh_TW
dc.date (日期) 2009
dc.date.accessioned 7-May-2015 17:40:17 (UTC+8)-
dc.date.available 7-May-2015 17:40:17 (UTC+8)-
dc.date.issued (上傳時間) 7-May-2015 17:40:17 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/75044-
dc.description.abstract (摘要) 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.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics,2695-2700
dc.subject (關鍵詞) 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
dc.title (題名) Classification of non-speech human sounds: Feature selection and snoring sound analysis
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1109/ICSMC.2009.5346556
dc.doi.uri (DOI) http://dx.doi.org/10.1109/ICSMC.2009.5346556