  Conventional sound source localization methods are mostly based on a single
microphone array that consists of multiple microphones. They are usually
formulated as the estimation of the direction of arrival problem. In this
paper, we propose a deep-learning-based end-to-end sound source localization
method with ad-hoc microphone arrays, where an ad-hoc microphone array is a set
of randomly distributed microphone arrays that collaborate with each other. It
can produce two-dimensional locations of speakers with only a single microphone
per node. Specifically, we divide a targeted indoor space into multiple local
areas. We encode each local area by a one-hot code, therefore, the node and
speaker locations can be represented by the one-hot codes. Accordingly, the
sound source localization problem is formulated as such a classification task
of recognizing the one-hot code of the speaker given the one hot codes of the
microphone nodes and their speech recordings. An end-to-end spatial-temporal
deep model is designed for the classification problem. It utilizes a
spatial-temporal attention architecture with a fusion layer inserted in the
middle of the architecture, which is able to handle arbitrarily different
numbers of microphone nodes during the model training and test. Experimental
results show that the proposed method yields good performance in highly
reverberant and noisy environments.
