Filter bank Kernel Learning for nonstationary signal classification

M. 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
Document type :
Conference papers
Complete list of metadatas
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, May 13, 2019 - 11:15:41 AM

Identifiers

Collections

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

Citation

M. 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⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

51

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD