  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.
