  While deep-learning-based speaker localization has shown advantages in
challenging acoustic environments, it often yields only direction-of-arrival
(DOA) cues rather than precise two-dimensional (2D) coordinates. To address
this, we propose a novel deep-learning-based 2D speaker localization method
leveraging ad-hoc microphone arrays, where an ad-hoc microphone array is
composed of randomly distributed microphone nodes, each of which is equipped
with a traditional array. Specifically, we first employ convolutional neural
networks at each node to estimate speaker directions. Then, we integrate these
DOA estimates using triangulation and clustering techniques to get 2D speaker
locations. To further boost the estimation accuracy, we introduce a node
selection algorithm that strategically filters the most reliable nodes.
Extensive experiments on both simulated and real-world data demonstrate that
our approach significantly outperforms conventional methods. The proposed node
selection further refines performance. The real-world dataset in the
experiment, named Libri-adhoc-node10 which is a newly recorded data described
for the first time in this paper, is online available at
https://github.com/Liu-sp/Libri-adhoc-nodes10.
