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Poster
Spline Filters For End-to-End Deep Learning
Randall Balestriero · Romain Cosentino · Herve Glotin · Richard Baraniuk
We propose to tackle the problem of end-to-end learning for raw waveforms signals by introducing learnable 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 freefield1010 dataset known to contain key challenges, such as high dimensional inputs ($>100000$) and the presence of multiple sources and soundscapes.
Author Information
Randall Balestriero (Rice University)
Romain Cosentino (Rice University)
Herve Glotin (Universite de Toulon)
Richard Baraniuk (OpenStax / Rice University)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: Spline Filters For End-to-End Deep Learning »
Wed. Jul 11th 03:50 -- 04:00 PM Room Victoria
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