  In this paper, we propose novel deep learning based algorithms for multiple
sound source localization. Specifically, we aim to find the 2D Cartesian
coordinates of multiple sound sources in an enclosed environment by using
multiple microphone arrays. To this end, we use an encoding-decoding
architecture and propose two improvements on it to accomplish the task. In
addition, we also propose two novel localization representations which increase
the accuracy. Lastly, new metrics are developed relying on resolution-based
multiple source association which enables us to evaluate and compare different
localization approaches. We tested our method on both synthetic and real world
data. The results show that our method improves upon the previous baseline
approach for this problem.
