Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Journal articles

Filter bank learning for signal classification

Abstract : This paper addresses the problem of feature extraction for signal classification. It proposes to build features by designing a data-driven filter bank and by pooling the time-frequency representation to provide time-invariant features. For this purpose, our work tackles the problem of jointly learning the filters of a filter bank with a support vector machine. It is shown that, in a restrictive case (but consistent to prevent overfitting), the problem boils down to a multiple kernel learning instance with infinitely many kernels. To solve such a problem, we build upon existing methods and propose an active constraint algorithm able to handle a non-convex combination of an infinite number of kernels. Numerical experiments on both a brain-computer interface dataset and a scene classification problem prove empirically the appeal of our method.
Complete list of metadata
Contributor : Marie-France Robbe Connect in order to contact the contributor
Submitted on : Monday, May 9, 2022 - 4:29:16 PM
Last modification on : Tuesday, May 10, 2022 - 9:51:21 AM


Files produced by the author(s)



Maxime Sangnier, Jérôme Gauthier, A. Rakotomamonjy. Filter bank learning for signal classification. Signal Processing, Elsevier, 2015, 113, pp.124 - 137. ⟨10.1016/j.sigpro.2014.12.028⟩. ⟨cea-01865050⟩



Record views


Files downloads