2D Acoustic Source Localisation Using Decentralised Deep Neural Networks on Distributed Microphone Arrays

Inhalt:
This paper takes a previously proposed convolutional recurrent deep neural network (DNN) approach to direction of arrival (DoA) estimation and extends this to perform 2D localisation using distributed microphone arrays. Triangulation on the individual DoAs from each array is the most straightforward extension of the original DNN. This paper proposes to allow more cooperation between the individual microphone arrays by sharing part of their neural network, in order to achieve a higher localisation accuracy. Two strategies will be discussed: one where the shared network has narrowband information, and one where only broadband information is shared. Robustness against slight clock offsets between different arrays is ensured by only sharing information at deeper layers in the DNN. The position and configuration of the microphone arrays are assumed known, in order to train the network. Simulations will show that combining information between neural network layers has a significant improvement over the triangulation approach.