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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.
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https://hal-cea.archives-ouvertes.fr/cea-01865050
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

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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⟩

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