Abstract
The crosstalk error is widely used to evaluate the performance of blind source separation. However, it needs to know the global separation matrix in advance, and it is not robust. In order to solve these problems, a new adaptive algorithm for calculating crosstalk error is presented, which calculates the crosstalk error by a cost function of least squares criterion, and the robustness of the crosstalk error is improved by introducing the position information of the maximum value in the global separation matrix. Finally, the method is compared with the conventional RLS algorithms in terms of performance, robustness and convergence rate. Furthermore, its validity is verified by simulation experiments and real world signals experiments.
Highlights
Blind signal separation (BSS) technology originated from the famous “cocktail party” problem (Choi and Cichocki, 1997) and has been a key research issue since (Bridwell et al, 2018; Yatabe and Kitamura, 2018)
The proximity of the estimation of PI calculated by our proposed algorithm and the theoretical value of PI calculated by the conventional recursive least square (RLS) algorithm is shown in Fig. 1, the value of the crosstalk error is the average of 500 Monte Carlo trials
It can be clearly observed that the estimation of the source signals are quite similar to the source signals, which indicates the effectiveness of our proposed algorithm
Summary
Blind signal separation (BSS) technology originated from the famous “cocktail party” problem (Choi and Cichocki, 1997) and has been a key research issue since (Bridwell et al, 2018; Yatabe and Kitamura, 2018). In order to overcome the above problems, Li Zong have proposed a method to improve crosstalk error evaluation criterion by introducing correlation coefficients to measure the degree of correlation between the separated signal components and adding them to the calculation formula of crosstalk error (Li et al, 2012). In order to evaluate the performance of the separation algorithms, the literature (Cichocki and Amari, 2002) gave a measurement method by using the difference between C and a generalized permutation matrix, which is named as crosstalk error and expressed as PI(C) = 1 n n. In this paper, we add the position information of the maximum value of each row in the C to the definition of the crosstalk error to improve the robustness of the crosstalk error
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
