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Conference papers
Filter bank Kernel Learning for nonstationary signal classification
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 :
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
https://hal-cea.archives-ouvertes.fr/cea-01830770
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Monday, May 9, 2022 - 4:09:56 PM
Last modification on : Wednesday, May 11, 2022 - 4:41:40 AM
Long-term archiving on: : Wednesday, August 10, 2022 - 8:12:37 PM
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Monday, May 9, 2022 - 4:09:56 PM
Last modification on : Wednesday, May 11, 2022 - 4:41:40 AM
Long-term archiving on: : Wednesday, August 10, 2022 - 8:12:37 PM
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