数据采集与处理2017，Vol.32Issue(3)：540-549,10.DOI:10.16337/j.1004-9037.2017.03.013
基于多核最小二乘支持向量回归的TDOA-DOA映射方法
TDOA-DOA Mapping Using Multi-kernel Least-Squares Support Vector Regression
摘要
Abstract
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 TDOADOA mapping is a crucial step.Here,we propose a TDOA-DOA mapping approach based on the multikernel least-squares support vector regression (LS-SVR),and also analyze its performance with sparsification.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 reverberant environments.Simulation results show that the proposed method is superior to its counterparts,such as LS and single-kernel LS-SVR methods.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Key words
sound source DOA estimation/TDOA estimation/least-squares support vector regression(LS-SVR)/multi-kernel learning分类
信息技术与安全科学引用本文复制引用
张峰,陈华伟,李妍文..基于多核最小二乘支持向量回归的TDOA-DOA映射方法[J].数据采集与处理,2017,32(3):540-549,10.基金项目
国家自然科学基金(61471190)资助项目. （61471190）
