  This paper presents SSLIDE, Sound Source Localization for Indoors using DEep
learning, which applies deep neural networks (DNNs) with encoder-decoder
structure to localize sound sources with random positions in a continuous
space. The spatial features of sound signals received by each microphone are
extracted and represented as likelihood surfaces for the sound source locations
in each point. Our DNN consists of an encoder network followed by two decoders.
The encoder obtains a compressed representation of the input likelihoods. One
decoder resolves the multipath caused by reverberation, and the other decoder
estimates the source location. Experiments based on both the simulated and
experimental data show that our method can not only outperform multiple signal
classification (MUSIC), steered response power with phase transform (SRP-PHAT),
sparse Bayesian learning (SBL), and a competing convolutional neural network
(CNN) approach in the reverberant environment but also achieve a good
generalization performance.
