Learning-based Acoustic Source Localization in Acoustic Sensor Networks using the Coherent-to-Diffuse Power Ratio
Andreas Brendel, Walter Kellermann · 2018
A distributed learning-based algorithm for the localization of acoustic sources in an acoustic sensor network is proposed. It is based on estimates of the Coherent-to-Diffuse Power Ratio (CDR), which serve as feature for the source-microphone distance, i.e., the range. The relation between the estimated CDR and the range is learned by using Gaussian processes for non-parametric regression. The range estimates obtained from evaluating the regression function are fused by a weighted least squares estimation, which is implemented recursively, allowing for a distributed version of the algorithm. The resulting method is computationally efficient, works in highly reverberant and noisy scenarios and needs only a small amount of data shared over the network. The training phase of the algorithm requires only a few labeled observations. We show the efficacy of the approach with data obtained from image-source simulation.