Spline Filters For End-to-End Deep Learning

Randall Balestriero, Romain Cosentino, Herv Glotin, Richard Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose to tackle the problem of end-to-end learning for raw waveforms signals by introducing lcamablc continuous time-frequency atoms. The derivation of these filters is achieved by first, defining a functional space with a given smoothness order and boundary conditions. From this space, we derive the parametric analytical filters. Their differentiability property allows gradient- based optimization. As such, one can equip any Deep Neural Networks (DNNs) with these filters. This enables .us to tackle in a front-end fashion a large scale bird detection task based on the freefieldlOlO dataset known to contain key challenges, such as high dimensional inputs (> 100000) and the presence of multiple sources and soundscapcs.

Original languageEnglish (US)
Title of host publication35th International Conference on Machine Learning, ICML 2018
EditorsAndreas Krause, Jennifer Dy
PublisherInternational Machine Learning Society (IMLS)
Pages636-645
Number of pages10
ISBN (Electronic)9781510867963
StatePublished - 2018
Event35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sweden
Duration: Jul 10 2018Jul 15 2018

Publication series

Name35th International Conference on Machine Learning, ICML 2018
Volume1

Other

Other35th International Conference on Machine Learning, ICML 2018
CountrySweden
CityStockholm
Period7/10/187/15/18

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software

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