Dynamically locating multiple speakers based on the time-frequency domain

In this study we present a deep neural network-based online multi-speaker localisation algorithm based on a multi-microphone array. A fully convolutional network is trained with instantaneous spatial features to estimate the direction of arrival for each time-frequency 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 simulated and real-life recordings in static and dynamic scenarios, demonstrates that the proposed algorithm significantly outperforms both classic and recent deep-learning-based algorithms.