Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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
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 compressing and storing them for
real-time rendering applications in augmented and virtual reality.

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The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
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The categories of this paper are: physics
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an