  In this work, we propose to extend a state-of-the-art multi-source
localization system based on a convolutional recurrent neural network and
Ambisonics signals. We significantly improve the performance of the baseline
network by changing the layout between convolutional and pooling layers. We
propose several configurations with more convolutional layers and smaller
pooling sizes in-between, so that less information is lost across the layers,
leading to a better feature extraction. In parallel, we test the system's
ability to localize up to 3 sources, in which case the improved feature
extraction provides the most significant boost in accuracy. We evaluate and
compare these improved configurations on synthetic and real-world data. The
obtained results show a quite substantial improvement of the multiple sound
source localization performance over the baseline network.
