School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, People's Republic of China.
J Acoust Soc Am. 2013 Sep;134(3):2066-77. doi: 10.1121/1.4816540.
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.
在不规则的麦克风阵列中，由于阵列增益模式和麦克风分布之间的复杂关系，限制了优化算法的应用。本文提出了一种用于沉浸式（近场）环境下麦克风阵列优化的遗传算法（GA）。本文提出了用于不规则阵列的几何描述符作为目标函数，通过避免直接计算阵列增益来减少优化时间。此外，还引入了声学场景的概率描述，以纳入声源分布的先验知识。为了验证所提出的优化方法的有效性，比较了 GA 优化阵列、规则阵列和通过直接穷举模拟优化的阵列的信噪比。结果表明，GA 优化阵列在任意随机生成的阵列和规则阵列上都有增强效果，尤其是在麦克风密度较低的情况下，放置变得至关重要。确定了 GA 的设计参数，以提高不同应用的优化稳健性。实验中观察到的快速收敛和可接受的处理时间证明了该方法在需要快速部署且对声学场景了解有限的沉浸式环境中优化阵列几何形状的可行性，例如在移动平台和音频监控应用中。
