论文标题
通过神经卡尔曼过滤的低复杂声音回声取消
Low-Complexity Acoustic Echo Cancellation with Neural Kalman Filtering
论文作者
论文摘要
卡尔曼滤波器因其鲁棒性对双对词，快速收敛和良好的稳态性能而被消除声音取消。卡尔曼过滤器的性能与状态噪声协方差的估计准确性和观察噪声协方差密切相关。估计误差可能会导致不可接受的结果，尤其是当回声路径遭受突然变化时，卡尔曼滤波器的跟踪性能可能会大大降低。在本文中，我们提出了神经卡尔曼滤波（NKF），该神经网络使用神经网络暗中对状态噪声和观察噪声的协方差进行建模，并实时输出卡尔曼的增益。合成测试集和真实录制的测试集的实验结果表明，所提出的NKF具有较高的收敛性和重新连接性能，同时确保与基于最先进的模型方法相比，近端近端语音降解较低。此外，提议的NKF的模型大小仅为5.3 K，RTF低至0.09，这表明它可以部署在低资源平台中。
The Kalman filter has been adopted in acoustic echo cancellation due to its robustness to double-talk, fast convergence, and good steady-state performance. The performance of Kalman filter is closely related to the estimation accuracy of the state noise covariance and the observation noise covariance. The estimation error may lead to unacceptable results, especially when the echo path suffers abrupt changes, the tracking performance of the Kalman filter could be degraded significantly. In this paper, we propose the neural Kalman filtering (NKF), which uses neural networks to implicitly model the covariance of the state noise and observation noise and to output the Kalman gain in real-time. Experimental results on both synthetic test sets and real-recorded test sets show that, the proposed NKF has superior convergence and re-convergence performance while ensuring low near-end speech degradation comparing with the state-of-the-art model-based methods. Moreover, the model size of the proposed NKF is merely 5.3 K and the RTF is as low as 0.09, which indicates that it can be deployed in low-resource platforms.
