论文标题
非线性声音回声取消的半盲源分离
Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellation
论文作者
论文摘要
最近提出的非线性声音回声取消（NAEC）的半盲源分离（SBSS）方法在衰减非线性声学回声方面的表现优于自适应NAEC。但是，乘法传输函数（MTF）近似使其不适合实时应用，尤其是在高度混响的环境中，并且自然梯度使得在快速收敛速度和稳定性之间很难平衡。在本文中，我们提出了两种基于基于辅助功能的独立矢量分析（Auxiva）和独立的低级矩阵分析（ILRMA）的更有效的SBS方法。使用备速传输函数（CTF）近似而不是MTF，以便可以用较短的延迟对长时间的脉冲响应进行建模。根据NAEC的约束矩阵，仔细地对Auxiva和ILRMA中使用的优化方案进行了仔细的正则。实验结果证明了所提出的方法的回声取消性能明显更好。
The recently proposed semi-blind source separation (SBSS) method for nonlinear acoustic echo cancellation (NAEC) outperforms adaptive NAEC in attenuating the nonlinear acoustic echo. However, the multiplicative transfer function (MTF) approximation makes it unsuitable for real-time applications especially in highly reverberant environments, and the natural gradient makes it hard to balance well between fast convergence speed and stability. In this paper, we propose two more effective SBSS methods based on auxiliary-function-based independent vector analysis (AuxIVA) and independent low-rank matrix analysis (ILRMA). The convolutive transfer function (CTF) approximation is used instead of MTF so that a long impulse response can be modeled with a short latency. The optimization schemes used in AuxIVA and ILRMA are carefully regularized according to the constrained demixing matrix of NAEC. Experimental results validate significantly better echo cancellation performance of the proposed methods.
