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Deep learning has been recently introduced for efficient acoustic howlingsuppression (AHS). However, the recurrent nature of howling creates a mismatchbetween offline training and streaming inference, limiting the quality ofenhanced speech. To address this limitation, we propose a hybrid method thatcombines a Kalman filter with a self-attentive recurrent neural network (SARNN)to leverage their respective advantages for robust AHS. During offlinetraining, a pre-processed signal obtained from the Kalman filter and an idealmicrophone signal generated via teacher-forced training strategy are used totrain the deep neural network (DNN). During streaming inference, the DNN'sparameters are fixed while its output serves as a reference signal for updatingthe Kalman filter. Evaluation in both offline and streaming inference scenariosusing simulated and real-recorded data shows that the proposed methodefficiently suppresses howling and consistently outperforms baselines.

Hybrid AHS: A Hybrid of Kalman Filter and Deep Learning for Acoustic Howling Suppression