针对助听器噪声环境，提出自适应语音增强模型，提升降噪效果。
DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids
- 根据背景噪声动态调整模型参数，实现个性化降噪
- 在DNS挑战数据集上显著优于传统统一模型
- 仅需少量额外计算，适合嵌入式助听设备
DeepFilterNet（DFN）是一种专为助听设备设计的深度学习降噪模型。尽管在多个基准测试中表现优异，但其采用‘一刀切’策略，使用单一模型泛化于各类噪声与环境，受限于模型规模和算力预算，泛化能力不足。近期研究发现，通过在上下文中利用外部录制的背景噪声信息进行条件调节，可有效提升降噪性能，且该过程可在助听器外完成，计算开销极小。本文将此机制引入DFN，提出新型的DFingerNet（DFiN）模型，在受DNS挑战启发的多个基准测试中展现出更优性能。
原文摘要 · Abstract (English)
The DeepFilterNet (DFN) architecture was recently proposed as a deep learning model suited for hearing aid devices. Despite its competitive performance on numerous benchmarks, it still follows a `one-size-fits-all' approach, which aims to train a single, monolithic architecture that generalises across different noises and environments. However, its limited size and computation budget can hamper its generalisability. Recent work has shown that in-context adaptation can improve performance by conditioning the denoising process on additional information extracted from background recordings to mitigate this. These recordings can be offloaded outside the hearing aid, thus improving performance while adding minimal computational overhead. We introduce these principles to the DFN model, thus proposing the DFingerNet (DFiN) model, which shows superior performance on various benchmarks inspired by the DNS Challenge.
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