Abstract
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.
Abstract (translated)
本研究提出一种多任务学习框架，用于优化单个深度神经网络（DNN），以实现噪声抑制（NR）与听力损失补偿（HLC）的联合处理。为每个任务分别定义训练目标，该DNN预测两个时频掩码。在推理阶段，通过分别对每个掩码进行指数运算后再合并，可独立调整NR和HLC的强度。与近期依赖训练听觉模型模拟器来定义可微分训练目标的方法不同，我们提出了一种本身具有可微性的听觉模型，从而实现端到端优化。听力图作为输入提供给DNN，使系统能够实现听者个性化定制而无需重新训练。结果表明，所提方法不仅支持单独调节NR和HLC强度，且在客观指标上优于单一训练目标优化方案。该方法还超越了分别针对NR和HLC训练的两个级联DNN，并在HLC性能上与传统助听器验配方案具有竞争力。据我们所知，这是首个利用听觉模型训练单个DNN，以覆盖广泛听者画像同时实现NR和HLC的研究。
URL
https://arxiv.org/abs/2603.20387
