Unmixed spectrum clustering for template composition in lung sound classification

Authors:
Tomonari Masada;Senya Kiyasu;Sueharu Miyahara
Affiliations:
Nagasaki University, Nagasaki, Japan;Nagasaki University, Nagasaki, Japan;Nagasaki University, Nagasaki, Japan
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

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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.