@article{oai:nagasaki-u.repo.nii.ac.jp:00016110,
 author = {Masada, Tomonari and Kiyasu, Senya and Miyahara, Sueharu},
 journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 month = {},
 note = {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., Advances in Knowledge Discovery and Data Mining. 
12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings, Lecture Notes in Computer Science, 5012, pp.964-969; 2008},
 pages = {964--969},
 title = {Unmixed spectrum clustering for template composition in lung sound classification},
 volume = {5012},
 year = {2008}
}