Source localization using the steered response power (SRP) usually requires a costly grid-search procedure. To address this issue, a modified SRP algorithm was recently introduced, providing improved robustness when using coarser spatial grids. In this letter, an iterative method based on the modified SRP is presented. A coarse spatial grid is initially evaluated with the modified SRP, selecting the point with the highest accumulated value. Then, its corresponding volume is iteratively decomposed by using a finer spatial grid. Experiments have shown that this method provides almost the same accuracy as the fine-grid search with a substantial reduction of functional evaluations.

Uniform circular array processing has been shown to be a very useful tool for broadband acoustic source localization over 360°. Specifically, beamforming methods based on circular harmonics have attracted a lot of research attention in the last several years, as modal array signal processing is a very active research topic. On the other hand, due to the sparsity properties of speech, source localization methods in the time–frequency (T–F) domain have also demonstrated their capability to locate several simultaneous sources with high accuracy. In this paper, a localization framework based on circular harmonics beamforming and T–F processing that provides accurate localization performance under very adverse acoustic conditions is presented. Modal processing and sparsity-based localization are jointly addressed to estimate the direction-of-arrival of multiple concurrent speech sources. Experiments in real and simulated environments with different microphone setups are discussed, showing the validity of the proposed approach and comparing its performance with other state-of-the-art methods.

Sound source localization using a two-microphone array is an active area of research, with considerable potential for use with video conferencing, mobile devices, and robotics. Based on the observed time-differences of arrival between sound signals, a probability distribution of the location of the sources is considered to estimate the actual source positions. However, these algorithms assume a given number of sound sources. This paper describes an updated research account on the solution presented in Escolano et al. [J. Acoust. Am. Soc. 132(3), 1257–1260 (2012)], where nested sampling is used to explore a probability distribution of the source position using a Laplacian mixture model, which allows both the number and position of speech sources to be inferred. This paper presents different experimental setups and scenarios to demonstrate the viability of the proposed method, which is compared with some of the most popular sampling methods, demonstrating that nested sampling is an accurate tool for speech localization.