混响噪声下声源定位与跟踪的多特征自适应IMM粒子滤波算法

An adaptive IMM particle filter algorithm based on multi-feature for sound source tracking in reverberant and noisy environments

中文关键词  声源定位  粒子滤波  多特征  室内混响  麦克风阵列  交互式多模型

英文关键词  sound source localization  particle filter  multi-feature  room reverberation  microphone array  interacting multiple model

中文摘要

      针对混响噪声下声源定位精度低和鲁棒性弱等问题, 提出了多特征自适应IMM粒子滤波算法. 该算法以麦 克风接收信号的多特征作为观测信息, 采用空时相关和迭代滤波建立了时延选择机制和波束输出能量优化机制, 并 在两者的基础上构建了似然函数以获得合理的声源位置信息. 考虑到说话人运动的随机性, 给出了自适应IMM算 法, 通过在线粒子集生成并将不同过程方差的模型进行交互来拟合说话人的不同运动模式, 改善了说话人跟踪系统 的稳健性. 仿真和实测结果表明, 所提算法利用了多特征定位信息的互补性, 降低了观测误差不确定性对声源位置 估计的影响, 增强了随机运动声源跟踪系统的鲁棒性, 提高了系统的定位精度.

英文摘要

      To deal with the problems of low accuracy and weak robustness of sound source localization in reverberant and noisy environments, an adaptive IMM particle filter algorithm based on the multi-feature is proposed. In this algorithm, the mechanisms of time delay selection and beam output energy optimization are established, by using the space-time correlation and iterative filtering, where the multi-features of the signals received by the microphones are exploited as the observation information. Subsequently, the reasonable sound source position information is obtained from the likelihood function, which is constructed on the basis of both. Meanwhile, considering the randomness of speaker motion, an adaptive IMM algorithm is given. By generating online particle set and interacting the models with different process variances, the speaker’s different motion modes are fitted, which improves the robustness of the speaker tracking system. The simulation and experimental results show that the complementarity of the location information based on the multi-feature is employed in the proposed algorithm, and the influence of the uncertainty of the observation error on sound source position estimation is reduced. Simultaneously, the robustness of random moving sound source tracking system is enhanced and the positioning accuracy of the system is improved.