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Unmixed spectrum clustering for template composition in lung sound classification

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タイトル: Unmixed spectrum clustering for template composition in lung sound classification
著者: Masada, Tomonari / Kiyasu, Senya / Miyahara, Sueharu
発行日: 2008年
出版者: Springer Verlag
引用: Lecture Notes in Computer Science, 5012, pp.964-969; 2008
抄録: In this paper, we propose a method for composing templates of lung sound classification. First, we obtain a sequence of power spectra by FFT for each given lung sound and compute a small number of component spectra by ICA for each of the overlapping sets of tens of consecutive power spectra. Second, we put component spectra obtained from various lung sounds into a single set and conduct clustering a large number of times. When component spectra belong to the same cluster in all clustering results, these spectra show robust similarity. Therefore, we can use such spectra to compose a template of lung sound classification.
記述: Advances in Knowledge Discovery and Data Mining. 12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings
URI: http://hdl.handle.net/10069/21988
ISBN: 978-3-540-68124-3
ISSN: 00218979
DOI: 10.1007/978-3-540-68125-0_100
権利: (c) 2008 Springer-Verlag Berlin Heidelberg. / The original publication is available at www.springerlink.com
資料タイプ: Journal Article
原稿種類: author
出現コレクション:060 学術雑誌論文

引用URI : http://hdl.handle.net/10069/21988



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