融合神经网络的卡尔曼滤波啸叫抑制路径突变检测算法OA北大核心CSTPCD

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,the Kalman filter at steady state might encounter the issue of deadlock when the feedback path experiences abrupt changes,exhibiting suboptimal tracking capabilities.In this paper,the Kalman-filter-based AFC with state detection model(KFSD)is proposed to effectively improve the robustness against abrupt path changes.The narrowband energy of the microphone signal,the residual signal and the update of Kalman filter are used as the input to the state detection model.And then,the state detection results are merged into the state estimation error covariance matrix of the Kalman filter,achieving better re-convergence performance against the abrupt path changes.Experimental results demonstrate the superior performance of the proposed KFSD 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 acoustic feedback cancellation..