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Filter bank Kernel Learning for nonstationary signal classification

Maxime Sangnier 1 J. Gauthier 2 A. Rakotomamonjy 1
1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
2 LADIS - Laboratoire d'analyse des données et d'intelligence des systèmes
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
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.
Keywords : Multiple Kernel Learning Nonstationary signals Optimization programs Signal classification SVM Time frequency domain Time-frequency representations Active filters Support vector machines Filter banks Automatic feature extraction
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Conference papers
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https://hal-cea.archives-ouvertes.fr/cea-01830770
Contributor : Léna Le Roy <>
Submitted on : Thursday, July 5, 2018 - 1:12:48 PM
Last modification on : Monday, March 30, 2020 - 3:44:03 PM

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CEA | LITIS | UNILEHAVRE | INSA-ROUEN | DRT | COMUE-NORMANDIE | LIST | UNIROUEN | INSA-GROUPE | DM2I

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Maxime Sangnier, J. Gauthier, A. Rakotomamonjy. Filter bank Kernel Learning for nonstationary signal classification. 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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