数据采集与处理2024，Vol.39Issue(5)：1126-1134,9.DOI:10.16337/j.1004-9037.2024.05.006
融合神经网络的卡尔曼滤波啸叫抑制路径突变检测算法
Kalman-Filter-Based Acoustic Feedback Cancellation with State Detection Model for Fast Recovery from Abrupt Path Changes
摘要
Abstract
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..关键词
声反馈抑制/自适应滤波/卡尔曼滤波/状态检测/深度神经网络Key words
acoustic feedback cancellation/adaptive filtering/Kalman filtering/state detection/deep neural network分类
信息技术与安全科学引用本文复制引用
郭昊诚,陈锴,卢晶..融合神经网络的卡尔曼滤波啸叫抑制路径突变检测算法[J].数据采集与处理,2024,39(5):1126-1134,9.基金项目
国家自然科学基金(12274221). （12274221）
