用自动微分与同心麦克风阵列实现宽频域的三维声束成形。
Frequency-Invariant Beamforming in Elevation and Azimuth via Autograd and Concentric Circular Microphone Arrays
- 结合自动微分与同心圆阵列，实现俯仰角与方位角联合优化。
- 在低频下显著提升俯仰方向主波束宽度控制能力。
- 适合需要精确双轴声源定位的语音增强与空间音频场景。
平面和同心圆麦克风阵列因能同时优化方位角与俯仰角，在声源定位与降噪等空间音频任务中备受关注。与仅限单轴调控的线性阵列不同，二维阵列支持双轴优化，但俯仰控制仍具挑战。本文将自动微分（autograd）与同心圆阵列结合，施加波束宽度与频率不变性约束，实现对两个角度的连续优化，并保持全频段性能稳定。通过仿真评估波束宽度、白噪声增益与指向性在多个频率下的表现。对比传统及先进波束成形方法（包括延迟求和、改进延迟求和、基于Jacobi-Anger展开的方法、高斯窗梯度下降法），本方法在空间选择性与主波束狭窄性方面表现更优，尤其在低频俯仰方向效果显著。结果表明，该方法有效提升了声学传感与需精确双轴控制的空间音频应用中的波束成形性能。
原文摘要 · Abstract (English)
The use of planar and concentric circular microphone arrays in beamforming has gained attention due to their ability to optimize both azimuth and elevation angles, making them ideal for spatial audio tasks like sound source localization and noise suppression. Unlike linear arrays, which restrict steering to a single axis, 2D arrays offer dual-axis optimization, although elevation control remains challenging. This study explores the integration of autograd, an automatic differentiation tool, with concentric circular arrays to impose beamwidth and frequency invariance constraints. This enables continuous optimization over both angles while maintaining performance across a wide frequency range. We evaluate our method through simulations of beamwidth, white noise gain, and directivity across multiple frequencies. A comparative analysis is presented against standard and advanced beamformers, including delay-and-sum, modified delay-and-sum, a Jacobi-Anger expansion-based method, and a Gaussian window-based gradient descent approach. Our method achieves superior spatial selectivity and narrower mainlobes, particularly in the elevation axis at lower frequencies. These results underscore the effectiveness of our approach in enhancing beamforming performance for acoustic sensing and spatial audio applications requiring precise dual-axis control.
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