Filter bank Kernel Learning for nonstationary signal classification

Maxime Sangnier (1, 2) , Jérôme Gauthier (2) , A. Rakotomamonjy (1)

1 LITIS - Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes
2 LOAD - Laboratoire Outils d'Analyse des Données

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: Time-frequency representations; Time frequency domain; SVM; Signal classification; Optimization programs; Nonstationary signals; Multiple Kernel Learning; Active filters; Support vector machines; Filter banks; Automatic feature extraction; machine learning; artificial intelligence; signal processing; classification

Domains: Signal and Image Processing; Information Retrieval [cs.IR]; Machine Learning [stat.ML]; Methodology [stat.ME]

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⟩