  In this paper, we present a new single sound source DOA estimation and
tracking system based on the well-known SRP-PHAT algorithm and a
three-dimensional Convolutional Neural Network. It uses SRP-PHAT power maps as
input features of a fully convolutional causal architecture that uses 3D
convolutional layers to accurately perform the tracking of a sound source even
in highly reverberant scenarios where most of the state of the art techniques
fail. Unlike previous methods, since we do not use bidirectional recurrent
layers and all our convolutional layers are causal in the time dimension, our
system is feasible for real-time applications and it provides a new DOA
estimation for each new SRP-PHAT map. To train the model, we introduce a new
procedure to simulate random trajectories as they are needed during the
training, equivalent to an infinite-size dataset with high flexibility to
modify its acoustical conditions such as the reverberation time. We use both
acoustical simulations on a large range of reverberation times and the actual
recordings of the LOCATA dataset to prove the robustness of our system and its
good performance even using low-resolution SRP-PHAT maps.
