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Unmixed spectrum clustering for template composition in lung sound classification


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Title: Unmixed spectrum clustering for template composition in lung sound classification
Authors: Masada, Tomonari / Kiyasu, Senya / Miyahara, Sueharu
Issue Date: 2008
Publisher: Springer Verlag
Citation: Lecture Notes in Computer Science, 5012, pp.964-969; 2008
Abstract: 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.
Description: 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
Rights: (c) 2008 Springer-Verlag Berlin Heidelberg. / The original publication is available at www.springerlink.com
Type: Journal Article
Text Version: author
Appears in Collections:Articles in academic journal

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

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