  Deep learning has been recently introduced for efficient acoustic howling
suppression (AHS). However, the recurrent nature of howling creates a mismatch
between offline training and streaming inference, limiting the quality of
enhanced speech. To address this limitation, we propose a hybrid method that
combines a Kalman filter with a self-attentive recurrent neural network (SARNN)
to leverage their respective advantages for robust AHS. During offline
training, a pre-processed signal obtained from the Kalman filter and an ideal
microphone signal generated via teacher-forced training strategy are used to
train the deep neural network (DNN). During streaming inference, the DNN's
parameters are fixed while its output serves as a reference signal for updating
the Kalman filter. Evaluation in both offline and streaming inference scenarios
using simulated and real-recorded data shows that the proposed method
efficiently suppresses howling and consistently outperforms baselines.
