Acoustic howling suppression (AHS) is a critical challenge in audio communication systems. In this paper, we propose a novel approach that leverages the power of neural networks (NN) to enhance the performance of traditional Kalman filter algorithms for AHS. Specifically, our method involves the integration of NN modules into the Kalman filter, enabling refining reference signal, a key factor in effective adaptive filtering, and estimating covariance metrics for the filter which are crucial for adaptability in dynamic conditions, thereby obtaining improved AHS performance. As a result, the proposed method achieves improved AHS performance compared to both standalone NN and Kalman filter methods. Experimental evaluations validate the effectiveness of our approach.

翻译：声反馈抑制是音频通信系统中的关键挑战。本文提出一种创新方法，通过利用神经网络增强传统卡尔曼滤波算法在声反馈抑制中的性能。具体而言，该方法将神经网络模块集成到卡尔曼滤波器中，既能够优化自适应滤波中的关键要素——参考信号，又能估计决定滤波器对动态环境适应能力的协方差参数，从而获得更优的声反馈抑制效果。实验评估验证了该方法的有效性，相较于独立运行的神经网络和卡尔曼滤波方法，所提算法展现出更优越的声反馈抑制性能。