End-to-End Multi-Task Learning for Adjustable Joint Noise Reduction and Hearing Loss Compensation
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a novel multi-task learning framework for joint noise reduction and hearing loss compensation using a single deep neural network. The approach's innovative use of a differentiable auditory model and listener-specific personalization is a significant contribution to the field, with promising experimental results that could lead to practical applications in hearing aids and auditory processing technologies.
Methodology
The paper introduces a multi-task learning framework that optimizes a single DNN for joint noise reduction (NR) and hearing loss compensation (HLC). The distinct training objectives for each task are well-defined, and the use of a differentiable auditory model for end-to-end optimization is a significant methodological advance. The incorporation of listener-specific audiograms as input for personalization without retraining is particularly innovative, showcasing a practical approach to tailoring solutions for individual users. The methodology is sound, but further details on the architecture and training process would enhance understanding.
Results & evaluation
The experiments conducted demonstrate a clear comparison between the proposed method and existing approaches, including a cascade of two separately trained DNNs and traditional hearing-aid prescriptions. The results indicate improvements in objective metrics, which are crucial for validating the effectiveness of the proposed framework. However, the paper could benefit from more extensive subjective evaluations (e.g., user studies) to complement the objective metrics and provide a holistic view of performance.
Reproducibility
The paper lacks detailed implementation specifics, such as hyperparameters, training data characteristics, and the exact architecture of the DNN. This omission makes it challenging to fully assess reproducibility. Including a supplementary material section with code, datasets, and configuration files would significantly enhance reproducibility.
Limitations
One limitation is the reliance on the audiogram as an input, which may not be available for all users. Additionally, while the results are promising, the paper does not address potential scalability issues or the performance of the model in real-world scenarios with diverse acoustic environments. The generalizability of the findings across different populations and hearing profiles also warrants further investigation.
Broader impact
The proposed framework has significant implications for the field of audiology and assistive technologies, potentially improving the quality of life for individuals with hearing loss. By enabling personalized adjustments to noise reduction and hearing compensation, this research could lead to more effective hearing aids and auditory devices. The integration of machine learning in this domain represents a step forward in the intersection of health technology and artificial intelligence. This paper presents a novel multi-task learning framework for joint noise reduction and hearing loss compensation using a single deep neural network. The approach's innovative use of a differentiable auditory model and listener-specific personalization is a significant contribution to the field, with promising experimental results that could lead to practical applications in hearing aids and auditory processing technologies.
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