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.

本文提出了基于深度学习的多声源定位算法，使用多个麦克风阵列找到封闭环境中多个声源的二维笛卡尔坐标，通过编码 - 解码结构和两个改进措施实现任务，并提出了两种新的定位表示方法，学习了新指标以实现分辨率基础上的多源关联和对比不同方法的评估。作者对合成和真实数据进行了测试，结果表明本方法改进了这一问题的基线方法。