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Spline Filters For End-to-End Deep Learning

Abstract : We propose to tackle the problem of end-to-end learning for raw waveform signals by introducing learnable continuous time-frequency atoms. The derivation of these filters is achieved by 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 utilize any Deep Neural Network (DNN) 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 the dimensionality of the inputs data (> 100, 000) and the presence of additional noises: multiple sources and soundscapes.
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Submitted on : Saturday, September 22, 2018 - 6:07:31 PM
Last modification on : Wednesday, January 8, 2020 - 4:18:01 PM
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  • HAL Id : hal-01879266, version 1



Randall Balestriero, Romain Cosentino, Hervé Glotin, Richard Baraniuk. Spline Filters For End-to-End Deep Learning. 35th International Conference on Machine Learning, Jul 2018, stockholm, Sweden. ⟨hal-01879266⟩



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