A hybrid descent method with genetic algorithm for microphone array placement design

In beamformer design, the microphone locations are often fixed and only the filter coefficients are varied in order to improve on the noise reduction performance. However, the positions of the microphone elements play an important role in the overall performance and should be optimized at the same time. However, this nonlinear optimization problem is non-convex and local search techniques might not yield the best result. This problem is addressed in this paper. A hybrid descent method is proposed which consists of a genetic algorithm together with a gradient-based method. The gradient-based method can help to locate the optimal solution rapidly around the start point, while the genetic algorithm is used to jump out from local minima. This hybrid method has the descent property and can help us to find the optimal placement for better beamformer design. Numerical examples are provided to demonstrate the effectiveness of the method.

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► We study the microphone array placement design problem.
► We propose a hybrid descent method with genetic algorithm.
► We show the designed beamformer perform better than other standard configurations.