Optimal irregular microphone distributions with enhanced beamforming performance in immersive environments.

Complex relationships between array gain patterns and microphone distributions limit the application of optimization algorithms on irregular arrays. This paper proposes a Genetic Algorithm (GA) for microphone array optimization in immersive (near-field) environments. Geometric descriptors for irregular arrays are proposed for use as objective functions to reduce optimization time by circumventing the need for direct array gain computations. In addition, probabilistic descriptions of acoustic scenes are introduced for incorporating prior knowledge of the source distribution. To verify the effectiveness of the proposed optimization, signal-to-noise ratios are compared for GA-optimized arrays, regular arrays, and arrays optimized through direct exhaustive simulations. Results show enhancements for GA-optimized arrays over arbitrary randomly generated arrays and regular arrays, especially at low microphone densities where placement becomes critical. Design parameters for the GA are identified for improving optimization robustness for different applications. The rapid convergence and acceptable processing times observed during the experiments establish the feasibility of this approach for optimizing array geometries in immersive environments where rapid deployment is required with limited knowledge of the acoustic scene, such as in mobile platforms and audio surveillance applications.