A Light-Weight State Detection Model for Kalman-Filter-Based Acoustic Feedback Cancellation with Rapid Recovery from Abrupt Path Changes

The partitioned block frequency domain Kalman Filter (PBFDKF) has been applied in acoustic feedback cancellation (AFC) due to its fast convergence and low steady-state misalignment. However, in cases where the feedback path experiences abrupt changes, the Kalman filter, once it reaches a steady state, might encounter the issue of deadlock and exhibit suboptimal tracking capabilities. In this paper, the Kalman filter with a light-weight state detection model (KF-SD) is proposed to effectively improve the robustness of AFC against abrupt path changes. The feedback return loss enhancement (FRLE) is proposed as the input to a state detection model with only 789 parameters to track the abrupt feedback path changes, and the state detection results are merged into the Kalman filter for a better re-convergence performance. A refined training label is proposed to ensure the robustness of model. Experimental results illustrate the superior performance of the proposed KF-SD algorithm, showcasing a high true positive rate, a low false alarm rate, and a short state detection latency. These advantages lead to faster re-convergence and enhanced sound quality when compared to the commonly used shadow filter strategy.