In an adaptive feedback cancellation (AFC) scenario, it is essential for an algorithm to track and cancel the feedback signal as quickly as possible. We analyze typical feedback paths in hearing aids and show that they exhibit a low-rank nature. Further, to exploit this knowledge and improve the convergence and tracking performance for AFC, we propose the nearest Kronecker product decomposition based adaptive feedback canceller with prediction error method based signal pre-whitening. Detailed simulation study and comparison of computational complexity show that the proposed algorithm can provide improved convergence and tracking along with improved output speech quality over traditional AFC algorithms, at a moderate computational load.

在自适应反馈消除（AFC）场景中，算法对于尽快跟踪和消除反馈信号至关重要。我们分析了助听器中的典型反馈路径，并显示它们表现出低等级的性质。此外，为了利用这一知识并提高AFC的收敛性和跟踪性能，我们提出了基于最近Kronecker产物分解的自适应反馈抵消器，并采用了基于信号预白化的预测误差方法。详细的仿真研究和计算复杂度的比较表明，与传统的AFC算法相比，该算法可以在中等计算量下提供改进的收敛性和跟踪能力，并提供改进的输出语音质量。