摘要
基于到达时间差(Time difference of arrival,TDOA)估计的方法是声源波达方向(Direction of arrival,DOA)估计中的一类重要方法。其中由TDOA到DOA的映射是该类方法的关键步骤。本文提出了一种基于多核聚类最小二乘支持向量回归(Least-squares support vector regression,LS-SVR)的TDOA-DOA映射方法,并且分析了其稀疏化处理后的性能。为了提高混响噪声环境下的TDOA-DOA映射性能,本文还给出了一种基于归一化中值滤波的TDOA估计离群值消除方法。仿真结果表明,本文提出的方法要优于现有的最小二乘方法以及单核LS-SVR方法。
In sound source direction of arrival （DOA） estimation, one of the typical methods is based on the time difference of arrival （TDOA）. For the TDOA-based sound source DOA estimation, the TDOA- DOA mapping is a crucial step. Here, we propose a TDOA-DOA mapping approach based on the multi- kernel least-squares support vector regression （LS-SVR）, and also analyze its performance with sparsifi- cation. In addition, we present an outlier detection method based on the normalized median filtering to post-process the TDOA estimation for improving the performance of TDOA-DOA mapping in noisy re- verberant environments. Simulation results show that the proposed method is superior to its counter- parts, such as LS and single-kernel LS-SVR methods.
作者
张峰
陈华伟
李妍文
Zhang Feng Chen Huawei Li Yanwen(College of Electronic and Information Engineering, Naniing University of Aeronautics and Astronautics, Naniing, 210016, China)
出处
《数据采集与处理》
CSCD
北大核心
2017年第3期540-549,共10页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(61471190)资助项目
关键词
声源波达方向估计
到达时间差估计
最小二乘支持向量回归
多核学习
sound source DOA estimation
TDOA estimation
least-squares support vector regression（LS-SVR）
multi-kernel learning
