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Class-specific model mixtures for the classification of time-series

: Baggenstoss, P.M.


European Association for Signal Processing -EURASIP-; Institute of Electrical and Electronics Engineers -IEEE-:
23rd European Signal Processing Conference, EUSIPCO 2015 : August 31 - September 4, 2015, Nice
Piscataway, NJ: IEEE, 2015
ISBN: 978-0-9928626-3-3
ISBN: 978-0-9928626-4-0
European Signal Processing Conference (EUSIPCO) <23, 2015, Nice>
Fraunhofer FKIE ()

We present a new classifier for acoustic time-series that involves a mixture of generative models. Each model operates on a feature stream extracted from the time-series using overlapped Hanning-weighted segments and has a probability density function (PDF) modeled with a hidden Markov model (HMM). The models use a variety of segmentation sizes and feature extraction methods, yet can be combined at a higher level using a mixture PDF thanks to the PDF projection theorem (PPT) that converts the feature PDF to raw time-series PDFs. The effectiveness of the method is shown using an open data set of short-duration acoustic signals.