A Novel Adaptive Crosstalk Cancellation using Psychoacoustic Model for 3D Audio

In rendering a virtual sound over two loudspeakers, adaptive inverse filtering is required for crosstalk cancellation. Although various adaptive algorithms have been proposed for crosstalk cancellation, few have been effective especially in time-varying environments where fast convergence rate is required. Until now, the least-mean square (LMS) algorithm known for its simplicity and robustness has been the predominant algorithm used, but its convergence rate is considered slow for colored inputs. In this paper, human perceptual characteristics which have never been incorporated in an LMS algorithm is introduced. In our experiment, the proposed algorithm achieved higher perceptual accuracy and faster convergence rate than the conventional LMS algorithm.