Abstract: Acoustic howling suppression (AHS) is a critical challenge in audio communication systems. In this study, 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.
This page provides sound demos for the titled paper. The titled paper can be accessed through [Link will be provided later.].
Although the output from the Kalman filter has been included, listening to the entire audio is not recommended.
| Waveforms | |
|---|---|
| G = 2 | |
| Target signal | |
| NeuralKalmanAHS | |
| NeuralKalmanAHS without \( \mathbf{\Psi}_{vv}(k) \), \( \mathbf{\Psi}_{\Delta\Delta}(k) \) | |
| NeuralKalmanAHS without \( \mathbf{R}(k) \) | |
| Kalman filter | |
Due to potential risks to the auditory system, we have omitted the 'no AHS' audio. Although we have included the outputs from the Kalman filter, listening to the entire audio is not recommended.
| Waveforms | ||
|---|---|---|
| Moderate Howling (G = 1.5) | Severe Howling (G = 3) | |
| Target signal | ||
| no AHS | ||
| Kalman filter | ||
| Deep MFC | ||
| Hybrid AHS | ||
| Neural-KG | ||
| NeuralKalmanAHS | ||