  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.
