Near-field localization of audio: A maximum likelihood approach

Localization of audio sources using microphone arrays has been an important research problem for more than two decades. Many traditional methods for solving the problem are based on a two-stage procedure: first, information about the audio source, such as time differences-of-arrival (TDOAs) and gain ratios-of-arrival (GROAs) between microphones is estimated, and, second, this knowledge is used to localize the audio source. These methods often have a low computational complexity, but this comes at the cost of a limited estimation accuracy. Therefore, we propose a new localization approach, where the desired signal is modeled using TDOAs and GROAs, which are determined by the source location. This facilitates the derivation of one-stage, maximum likelihood methods under a white Gaussian noise assumption that is applicable in both near- and far-field scenarios. Simulations show that the proposed method is statistically efficient and outperforms state-of-the-art estimators in most scenarios, involving both synthetic and real data.