多特征下室内声源定位的复合模型粒子滤波  

Composite Model Particle Filter for Indoor Sound Source Location Based on Multi-feature

作　　者：刘望生 潘海鹏[1] 王明环[2] LIU Wangsheng;PAN Haipeng;WANG Minghuan(School of Information Science and Engineering,Zhejiang Sci-Tech University,Hangzhou 310018,Zhejiang,China;Key Laboratory of Special Purpose Equipment and Advanced Processing Technology of Ministry of Education,Zhejiang University of Technology,Hangzhou 310012,Zhejiang,China)

机构地区：[1]浙江理工大学信息科学与工程学院,浙江杭州310018 [2]浙江工业大学特种装备制造与先进加工技术教育部重点实验室,浙江杭州310012

出　　处：《兵工学报》2024年第3期975-985,共11页Acta Armamentarii

基　　金：国家自然科学基金项目(51975532)。

摘　　要：为提高混响噪声下声源定位的精度和稳健性,提出多特征复合模型粒子滤波算法。该算法以麦克风接收信号的多特征构建似然函数,采用卷积神经网络提取多假设时延估计图像的深度特征,建立基于支持向量回归的时延估计模型;引入波束输出能量融合机制,弥补单特征不能同时抑制噪声和混响的缺陷。针对说话人运动随机性的问题建立声源跟踪的复合模型,改善说话人跟踪系统的鲁棒性。仿真和实测结果表明:在复合模型跟踪下,多特征算法比可控响应功率时延估计算法位置平均均方根误差减少83%以上;在多特征观测下,复合模型比郎之万模型和随机行走模型位置平均均方根误差减少46%以上;新算法实现了对复杂环境下随机运动声源的有效跟踪。A multi-feature-based composite model particle filter algorithm is proposed to improve the accuracy and robustness of sound source location in reverberation and noise environment.In this algorithm,the likelihood function of the particle filter is constructed based on the multiple features of signal received by a microphone,where the depth features of multiple hypothesis time-delay estimated image are extracted by convolutional neural network(CNN),and a time-delay estimation model based on support vector regression(SVR)is established.Furthermore,the deficiency that single feature can t suppress noise and reverberation simultaneously is remedied by introducing the beam output energy fusion mechanism.For the randomness of speaker motion,a composite model for sound source tracking is established to improve the robustness of speaker tracking system.The simulated and experimental results show that,based on the composite model,the position average root mean square error(RMSE)of multi-feature algorithm is reduced by more than 83%compared with that of steered response power and time delay estimation(SRPTDE)algorithm,and under multi-feature observation,the position average RMSE of composite model is reduced by more than 46%compared with that of Langevin model and the random walking model.The proposed algorithm realizes the effective tracking of random moving sound sources in complex environment.

关 键 词：室内声源定位 多特征 时延估计 复合模型 粒子滤波 

分 类 号：TN272[电子电信—物理电子学]