  In this paper, we present a deep neural network-based online multi-speaker
localisation algorithm. Following the W-disjoint orthogonality principle in the
spectral domain, each time-frequency (TF) bin is dominated by a single speaker,
and hence by a single direction of arrival (DOA). A fully convolutional network
is trained with instantaneous spatial features to estimate the DOA for each TF
bin. The high resolution classification enables the network to accurately and
simultaneously localize and track multiple speakers, both static and dynamic.
Elaborated experimental study using both simulated and real-life recordings in
static and dynamic scenarios, confirms that the proposed algorithm outperforms
both classic and recent deep-learning-based algorithms.
