While the semi-blind source separation-based acoustic echo cancellation (SBSS-AEC) has received much research attention due to its promising performance during double-talk compared to the traditional adaptive algorithms, it suffers from system latency and nonlinear distortions. To circumvent these drawbacks, the recently developed ideas on convolutive transfer function (CTF) approximation and nonlinear expansion have been used in the iterative projection (IP)-based semi-blind source separation (SBSS) algorithm. However, because of the introduction of CTF approximation and nonlinear expansion, this algorithm becomes computationally very expensive, which makes it difficult to implement in embedded systems. Thus, we attempt in this paper to improve this IP-based algorithm, thereby developing an element-wise iterative source steering (EISS) algorithm. In comparison with the IP-based SBSS algorithm, the proposed algorithm is computationally much more efficient, especially when the nonlinear 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.

翻译：尽管基于半盲源分离的声学回声消除（SBSS-AEC）由于其在双讲场景中相比传统自适应算法具有更优性能而受到广泛研究关注，但其仍存在系统延迟和非线性失真问题。为克服这些缺陷，近年来提出的卷积传递函数（CTF）近似和非线性展开思想已被应用于基于迭代投影（IP）的半盲源分离（SBSS）算法中。然而，由于引入CTF近似和非线性展开，该算法计算开销极大，难以在嵌入式系统中实现。因此，本文尝试改进该基于IP的算法，进而提出一种逐元素迭代源导向（EISS）算法。与基于IP的SBSS算法相比，所提算法在计算效率上显著提升，尤其在非线性展开阶数较高且CTF滤波器长度较长时表现更为突出。同时，其AEC性能与基于IP的SBSS算法相当。