Near field acoustic localization under unfavorable conditions using feedforward neural network for processing time difference of arrival

Using time difference of arrival (TDOA) is one of the two approaches that utilize time delay for acoustic source localization. Combining the obtained TDOAs together with geometrical relationships within acoustic components results in a system of hyperbolic equations. Solving these hyperbolic equations is not a trivial procedure especially in the case of a large number of microphones. The solution is additionally compounded by uncertainties of different backgrounds. The paper investigates the performance of neural networks in modelling a hyperbolic positioning problem using a feedforward neural network as a representative. For experimental purposes, more than 2000 sound files were recorded by 8 spatially disposed microphones, for as many arbitrarily chosen acoustic source positions. The samples were corrupted by high level correlated noise and reverberation. Using cross-correlation, with previous signal pre-processing, TDOAs were evaluated for every pair of microphones. On the basis of the obtained TDOAs and accurate sound source positions, the neural network was trained to perform sound source localization. The performance was examined using a large number of samples in terms of different acoustic sensors setups, network configurations and training parameters. The experiment provided useful guidelines for the practical implementation of feedforward neural networks in the near-field acoustic localization. The procedure does not require substantial knowledge of signal processing and that is why it is suitable for a broad range of users.