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Filter bank Kernel Learning for nonstationary signal classification

Abstract : This paper addresses the problem of automatic feature extraction for signal classification. In order to handle non-stationarity, features are designed in the time-frequency domain using a Filter Bank as the mapping function, which enables an easy interpretation for practitioners. The strategy adopted is to jointly learn a Filter Bank with a Support Vector Machine by casting the optimization program as a Multiple Kernel Learning problem. This solves the program for a finite set of filters. Thus, in order to handle an infinite number of filters, a novel active constraint algorithm is proposed based on the latest breakthroughs. Our method has been tested on a toy dataset and compared to classical methods with competitive results.
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Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Monday, May 9, 2022 - 4:09:56 PM
Last modification on : Wednesday, May 11, 2022 - 4:41:40 AM
Long-term archiving on: : Wednesday, August 10, 2022 - 8:12:37 PM


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Maxime Sangnier, Jérôme Gauthier, A. Rakotomamonjy. Filter bank Kernel Learning for nonstationary signal classification. ICASSP 2013 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2013, Vancouver, BC, Canada. pp.3183-3187, ⟨10.1109/ICASSP.2013.6638245⟩. ⟨cea-01830770⟩



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