Pu Henglin, Cai Chao, Hu Menglan, Deng Tianping, Zheng Rong, Luo Jun
School of Electronic Information and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Sensors (Basel). 2021 Jan 13;21(2):532. doi: 10.3390/s21020532.
Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.
多盲声源定位是机器人导航和室内定位等众多应用中的关键技术。然而，由于阵列中麦克风数量的限制，现有解决方案只能同时定位少数声源。为此，本文提出了一种使用源分离和波束形成（SPBF）的新型多盲声源定位算法。我们的算法克服了现有解决方案的局限性，能够定位比阵列中麦克风数量更多的盲声源。具体而言，我们提出了一种新颖的麦克风布局，在仍然保留声源到达时间信息的同时，实现显著的多源分离。然后，我们使用每个分离后的声源通过波束形成进行声源定位。这种设计可以最大限度地减少来自不同声源的相互干扰，从而实现更精确的到达角估计。为了进一步提高定位性能，我们设计了一种新的频谱加权函数，该函数可以提高信噪比，允许使用相对较窄的波束，从而实现更精确的到达角估计。典型室内场景下的仿真实验表明，即使在多达14个声源的情况下，最大误差也仅为4°。
