Acoustic beamforming aims to focus acoustic signals to a specific direction and suppress undesirable interferences from other directions. Despite its flexibility and steerability, beamforming with circular microphone arrays suffers from significant performance degradation at frequencies corresponding to zeros of the Bessel functions. To conquer this constraint, baffled or concentric circular microphone arrays have been studied; however, the former needs a bulky baffle that interferes with the original sound field whereas the latter requires more microphones that increase the complexity and cost, both of which are undesirable in practical applications. To tackle this challenge, this paper proposes a circular microphone array equipped with virtual microphones, which resolves the performance degradation commonly associated with circular microphone arrays without resorting to physical modifications. The sound pressures at the virtual microphones are predicted from those measured by the physical microphones based on an acoustics-informed neural network, and then the sound pressures measured by the physical microphones and those predicted at the virtual microphones are integrated to design the beamformer. Experimental results demonstrate that the proposed approach not only eliminates the performance degradation but also suppresses spatial aliasing at high frequencies, thereby underscoring its promising potential.

翻译：声学波束形成旨在将声学信号聚焦于特定方向，并抑制来自其他方向的不期望干扰。尽管圆形麦克风阵列的波束形成具有灵活性和可转向性，但其在贝塞尔函数零点对应频率处会出现显著的性能下降。为解决这一限制，研究者提出了障板式或同心圆式麦克风阵列；然而，前者需要体积庞大的障板从而干扰原始声场，后者则需更多麦克风增加复杂度与成本，二者在实际应用中均不理想。为应对这一挑战，本文提出了一种配备虚拟麦克风的圆形麦克风阵列，在不进行物理修改的情况下解决了圆形麦克风阵列常见的性能退化问题。虚拟麦克风处的声压通过基于声学神经网络从物理麦克风测量值预测获得，然后将物理麦克风测量声压与虚拟麦克风预测声压整合以设计波束形成器。实验结果表明，所提方法不仅消除了性能退化，还抑制了高频空间混叠，充分展现了其应用潜力。