用单一模型同时调节降噪与听力补偿，可个性化适配不同用户。
End-to-End Multi-Task Learning for Adjustable Joint Noise Reduction and Hearing Loss Compensation
- 设计可微分的听觉模型，实现端到端联合训练。
- 独立调节降噪和补偿强度，性能优于单目标优化和级联模型。
- 支持个性化配置，无需重训即可适配不同听力损失者。
提出一种多任务学习框架，通过单个深度神经网络（DNN）联合优化降噪（NR）与听力损失补偿（HLC）。每个任务定义独立训练目标，DNN预测两个时频掩码。推理时，通过指数化各掩码后组合，可独立调整降噪与补偿程度。不同于依赖听觉模型模拟器的方法，本文提出一个固有可微的听觉模型，实现端到端优化。将听觉图（audiogram）作为输入，实现无需重训的用户个性化。结果表明，该方法不仅支持独立调节降噪与补偿量，且在客观指标上优于单目标优化；同时优于分别训练的两个DNN级联结构，并在HLC性能上达到传统助听器处方水平。据我们所知，这是首个基于听觉模型，在广泛用户群体中训练单一DNN实现联合降噪与补偿的研究。
原文摘要 · Abstract (English)
A multi-task learning framework is proposed for optimizing a single deep neural network (DNN) for joint noise reduction (NR) and hearing loss compensation (HLC). A distinct training objective is defined for each task, and the DNN predicts two time-frequency masks. During inference, the amounts of NR and HLC can be adjusted independently by exponentiating each mask before combining them. In contrast to recent approaches that rely on training an auditory-model emulator to define a differentiable training objective, we propose an auditory model that is inherently differentiable, thus allowing end-to-end optimization. The audiogram is provided as an input to the DNN, thereby enabling listener-specific personalization without the need for retraining. Results show that the proposed approach not only allows adjusting the amounts of NR and HLC individually, but also improves objective metrics compared to optimizing a single training objective. It also outperforms a cascade of two DNNs that were separately trained for NR and HLC, and shows competitive HLC performance compared to a traditional hearing-aid prescription. To the best of our knowledge, this is the first study that uses an auditory model to train a single DNN for both NR and HLC across a wide range of listener profiles.
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