  This paper proposes a deep neural network for estimating the directions of
arrival (DOA) of multiple sound sources. The proposed stacked convolutional and
recurrent neural network (DOAnet) generates a spatial pseudo-spectrum (SPS)
along with the DOA estimates in both azimuth and elevation. We avoid any
explicit feature extraction step by using the magnitudes and phases of the
spectrograms of all the channels as input to the network. The proposed DOAnet
is evaluated by estimating the DOAs of multiple concurrently present sources in
anechoic, matched and unmatched reverberant conditions. The results show that
the proposed DOAnet is capable of estimating the number of sources and their
respective DOAs with good precision and generate SPS with high signal-to-noise
ratio.
