分布式麦克风网络中降噪低复杂度能量感知传感器选择  

Low-complexity energy-aware sensor selection for noise reduction in distributed microphone networks

作　　者：张结 许露真 戴礼荣[1] Jie Zhang;Lu-Zhen Xu;Li-Rong Dai(National Engineering Research Center for Speech and Language Information Processing,University of Science and Technology of China,Hefei 230027,China)

机构地区：[1]中国科学技术大学语音及语言信息处理国家工程研究中心,安徽合肥230027

出　　处：《中国科学技术大学学报》2023年第4期12-21,I0008,共11页JUSTC

基　　金：supported by the National Natural Science Foundation of China (62101523);Hefei Municipal Natural Science Foundation (2022012);Fundamental Research Funds for the Central Universities (WK2100000016);USTC Research Funds of the Double First-Class Initiative (YD2100002008)。

摘　　要：降噪(NR)是许多音频应用系统改善信号质量的必要前端模块。研究表明,稀疏促进的传感器选择方法具有权衡能量消耗和降噪性能的能力,这对大规模无线声学传感器网络(WASNs)非常重要,因为其中很多传感器对降噪的贡献甚微,但是能耗会影响无线声学传感器网络(WASNs)的生存期。本文通过最小化总体能耗和约束输出噪声方差的方式提出了一种基于传感器选择的波束形成降噪(NR)方法。受最优半定规划解(SDP)和实体方法的启发,我们提出了三种低复杂度选择度量准则:加权实体、梯度、加权输入信噪比(SNR)。可以证明,所提基于加权实体和梯度的方法在性能上是近最优的,但是比半定规划方法(SDP)更快,加权信噪比方法以牺牲微弱性能代价换取了最低的时间复杂度。基于仿真无线声学传感器网络(WASN)的数值结果验证了所提方法相对于传统方法的优势。Noise reduction(NR) is a necessary front-end in many audio applications for improving signal quality. It was shown that sparsity-promoting sensor selection potentially makes a trade-off between energy consumption and NR performance, which is rather important for large-scale wireless acoustic sensor networks(WASNs), where many sensors contribute negligibly to NR but energy consumption affects the lifetime of WASNs. This paper presents a sensor selection approach for beamforming-based NR by minimizing the total energy consumption and constraining the output noise variance. Motivated by the optimal semi-definite programming(SDP) solution and the utility-based method, we propose three low-complexity selection metrics: weighted utility, gradient, and weighted input signal-to-noise ratio(SNR). It is shown that the proposed weighted utility and gradient-based methods are near-optimal in performance but much faster than the SDP-based method, and the weighted SNR method has the lowest time complexity with a tiny performance sacrifice. Numerical results using a simulated WASN validate the superiority of the proposed approaches over conventional methods.

关 键 词：传感器选择 前向/后向算法 梯度 实体 MVDR波束形成 语音增强 分布式麦克风阵列 

分 类 号：TN912.35[电子电信—通信与信息系统]