Presented here is a novel clustering method for Hidden Markov Models (HMMs) and its application in acoustic scene analysis. In this method, HMMs are clustered based on a similarity measure for stochastic models defined as the generalized probability product kernel (GPPK), which can be efficiently evaluated according to a fast algorithm introduced by Chen and Man (2005) [1]. Acoustic signals from various sources are partitioned into small frames. Frequency features are extracted from each of the frames to form observation vectors. These frames are further grouped into segments, and an HMM is trained from each of such segments. An unknown segment is categorized with a known event if its HMM has the closest similarity with the HMM from the corresponding labeled segment. Experiments are conducted on an underwater acoustic dataset from Steven Maritime Security Laboratory, Data set contains a swimmer signature, a noise signature from the Hudson River, and a test sequence with a swimmer in the Hudson River. Experimental results show that the proposed method can successfully associate the test sequence with the swimmer signature at very high confidence, despite their different time behaviors.
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