Abstract
A multichannel filtered-x least mean square (LMS) algorithm is an efficient feedforward algorithm in an active noise control (ANC) system, whose convergence rate is known to be limited by many factors, such as a secondary path model and the correlation between reference signals. In this paper, we introduce an adaptive blind preprocessing method for reducing the eigenvalue spread of the correlation matrix of reference signals, which is often ignored in a typical multichannel ANC algorithm. Two blind adaptive decorrelation algorithms are derived for different reference path models. Numerical experiments verify the robust performance of the proposed preprocessing methods, including reducing the mean square error and improving the convergence speed.
