A Data-Reuse Semi-Blind Source Separation Approach for Nonlinear Acoustic Echo Cancellation

Nonlinear acoustic echo cancellation (NAEC) is of significant importance in acoustic telecommunication. To improve NAEC performance in the double-talk case, semi-blind source separation-based NAEC (SBSS-NAEC) algorithms have been proposed. However, to deal with reverberation and loudspeaker nonlinearities, convolutive transfer function (CTF) models and power series expansions are employed, which significantly increase the number of free parameters and consequently lead to slow convergence speed and, hence, limited performance. In this paper, we introduce the data-reuse strategy, well-known in the adaptive filter literature, into an SBSS-NAEC framework and propose two algorithms: data-reuse iteration projection (DR-IP) and data-reuse element-wise iterative source steering (DR-EISS). Several simulations demonstrate the superiority of the proposed methods, especially the tracking capability when the impulse response changes.