Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Neural Networks for Multiple Speaker Detection and Localization   We propose to use neural networks for simultaneous detection and localization
of multiple sound sources in human-robot interaction. In contrast to
conventional signal processing techniques, neural network-based sound source
localization methods require fewer strong assumptions about the environment.
Previous neural network-based methods have been focusing on localizing a single
sound source, which do not extend to multiple sources in terms of detection and
localization. In this paper, we thus propose a likelihood-based encoding of the
network output, which naturally allows the detection of an arbitrary number of
sources. In addition, we investigate the use of sub-band cross-correlation
information as features for better localization in sound mixtures, as well as
three different network architectures based on different motivations.
Experiments on real data recorded from a robot show that our proposed methods
significantly outperform the popular spatial spectrum-based approaches.

3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org