Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Acoustic-Net: A Novel Neural Network for Sound Localization and
  Quantification   Acoustic source localization has been applied in different fields, such as
aeronautics and ocean science, generally using multiple microphones array data
to reconstruct the source location. However, the model-based beamforming
methods fail to achieve the high-resolution of conventional beamforming maps.
Deep neural networks are also appropriate to locate the sound source, but in
general, these methods with complex network structures are hard to be
recognized by hardware. In this paper, a novel neural network, termed the
Acoustic-Net, is proposed to locate and quantify the sound source simply using
the original signals. The experiments demonstrate that the proposed method
significantly improves the accuracy of sound source prediction and the
computing speed, which may generalize well to real data. The code and trained
models are available at https://github.com/JoaquinChou/Acoustic-Net.

27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn