Research Article
A Single Source Point Detection Algorithm for Underdetermined Blind Source Separation Problem
@INPROCEEDINGS{10.1007/978-3-030-19086-6_8, author={Yu Zhang and Zhaoyue Zhang and Hongxu Tao and Yun Lin}, title={A Single Source Point Detection Algorithm for Underdetermined Blind Source Separation Problem}, proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings}, proceedings_a={ADHIP}, year={2019}, month={5}, keywords={Time-frequency domain Mixing matrix estimation Single source points detection}, doi={10.1007/978-3-030-19086-6_8} }- Yu Zhang
Zhaoyue Zhang
Hongxu Tao
Yun Lin
Year: 2019
A Single Source Point Detection Algorithm for Underdetermined Blind Source Separation Problem
ADHIP
Springer
DOI: 10.1007/978-3-030-19086-6_8
Abstract
To overcome the traditional disadvantages of single source points detection methods in underdetermined blind source separation problem, this paper proposes a novel algorithm to detect single source points for the linear instantaneous mixed model. First, the algorithm utilizes a certain relationship between the time-frequency coefficients and the complex conjugate factors of the observation signal to realize single source points detection. Then, the algorithm finds more time-frequency points that meets the requirements automatically and cluster them by utilizing a clustering algorithm based on the improved potential function. Finally, the estimation of the mixed matrix is achieved by clustering the re-selected single source points. Simulation experiments on linear mixture model demonstrates the efficiency and feasibility for estimating the mixing matrix.
