用可微听觉模型同时降噪和补偿听力损失，还能灵活调节两者权重。
Controllable joint noise reduction and hearing loss compensation using a differentiable auditory model
- 将降噪与听力补偿建模为多任务学习，通过可微听觉模型联合优化。
- 在相同指标下表现接近单独训练的系统，且推理时可动态调整任务平衡。
- 适合需要个性化听觉增强的场景，尤其对听力受损者有实用价值。
基于深度学习的听力损失补偿（HLC）旨在通过神经网络提升听障人士的语音可懂度和质量。然而，HLC面临缺乏真实目标的问题。现有方法虽利用神经网络模拟不可微的听觉外周模型，但灵活性不足；而可微听觉模型虽支持直接优化，但以往研究仅针对单一用户或未平衡降噪（NR）与HLC任务。本文将NR与HLC构建成多任务学习问题，使用可微听觉模型，从含噪语音和听力图中联合预测去噪与补偿信号。结果表明，该系统在客观指标上达到与独立训练系统相当的性能，且可在推理阶段灵活调整降噪与补偿的平衡比例。
原文摘要 · Abstract (English)
Deep learning-based hearing loss compensation (HLC) seeks to enhance speech intelligibility and quality for hearing impaired listeners using neural networks. One major challenge of HLC is the lack of a ground-truth target. Recent works have used neural networks to emulate non-differentiable auditory peripheral models in closed-loop frameworks, but this approach lacks flexibility. Alternatively, differentiable auditory models allow direct optimization, yet previous studies focused on individual listener profiles, or joint noise reduction (NR) and HLC without balancing each task. This work formulates NR and HLC as a multi-task learning problem, training a system to simultaneously predict denoised and compensated signals from noisy speech and audiograms using a differentiable auditory model. Results show the system achieves similar objective metric performance to systems trained for each task separately, while being able to adjust the balance between NR and HLC during inference.
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