In-the-Loop Training of Deep Feedback Cancellation for Hearing Aids

Svantje Voit; Simon Doclo

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arXiv · 2606.03832

In-the-Loop Training of Deep Feedback Cancellation for Hearing Aids

Svantje Voit·Simon Doclo

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Abstract

Acoustic feedback limits the maximum gain in hearing aids. In addition to several approaches based on adaptive filtering, recently a deep-neural-network-based feedback cancellation (DFC) approach has been proposed, which is trained via an open-loop framework. Since open-loop-trained DFC (DFC-OL) can become unstable during inference at high gains, in this paper we propose an in-the-loop-trained DFC (DFC-IL) that integrates the DFC directly into the optimisation loop. This allows the model to be exposed to unstable conditions during training. A two-stage training strategy involving pre-training on stable systems and fine-tuning on a wider gain range enables DFC-IL to learn robust howling reduction. Experimental results on measured feedback paths demonstrate that in scenarios with small gains, the proposed DFC-IL performs similarly to DFC-OL, and both exceed the performance of adaptive filters. In scenarios with high amplification gains, DFC-IL clearly outperforms DFC-OL by maintaining system stability.

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BibTeX RIS

Svantje Voit, Simon Doclo. 2026-06-02. In-the-Loop Training of Deep Feedback Cancellation for Hearing Aids. https://arxiv.org/abs/2606.03832

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