Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks

Impulse response estimation in high noise and in-the-wild settings, with minimal control of the underlying data distributions, is a challenging problem. We propose a novel framework for parameterizing and estimating impulse responses based on recent advances in neural representation learning. Our fr...

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Bibliographic Details
Main Authors: Richard, Alexander, Dodds, Peter, Ithapu, Vamsi Krishna
Format: Conference Proceeding
Language:English
Subjects:
Estimation
filter interpolation
impulse response estimation
Neural networks
neural representation learning
Rendering (computer graphics)
Representation learning
Robustness
Signal processing
Virtual reality
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Description
Summary:Impulse response estimation in high noise and in-the-wild settings, with minimal control of the underlying data distributions, is a challenging problem. We propose a novel framework for parameterizing and estimating impulse responses based on recent advances in neural representation learning. Our framework is driven by a carefully designed neural network that jointly estimates the impulse response and the (apriori unknown) spectral noise characteristics of an observed signal given the source signal. We demonstrate robustness in estimation, even under low signal-to-noise ratios, and show strong results when learning from spatio-temporal real-world speech data. Our framework provides a natural way to interpolate impulse responses on a spatial grid, while also allowing for efficiently com-pressing and storing them for real-time rendering applications in augmented and virtual reality.
ISSN:2379-190X
DOI:10.1109/ICASSP43922.2022.9746135