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While the semi-blind source separation-based acoustic echo cancellation(SBSS-AEC) has received much research attention due to its promisingperformance during double-talk compared to the traditional adaptive algorithms,it suffers from system latency and nonlinear distortions. To circumvent thesedrawbacks, the recently developed ideas on convolutive transfer function (CTF)approximation and nonlinear expansion have been used in the iterativeprojection (IP)-based semi-blind source separation (SBSS) algorithm. However,because of the introduction of CTF approximation and nonlinear expansion, thisalgorithm becomes computationally very expensive, which makes it difficult toimplement in embedded systems. Thus, we attempt in this paper to improve thisIP-based algorithm, thereby developing an element-wise iterative sourcesteering (EISS) algorithm. In comparison with the IP-based SBSS algorithm, theproposed algorithm is computationally much more efficient, especially when thenonlinear expansion order is high and the length of the CTF filter is long.Meanwhile, its AEC performance is as good as that of IP-based SBSS.

A computationally efficient semi-blind source separation based approach  for nonlinear echo cancellation based on an element-wise iterative source  steering