The great success of deep learning-based noise reduction algorithms makes it desirable to also use them for hearing aid applications. However, neural networks are both computationally intensive and memory intensive, making it challenging to deploy on an embedded system with limited hardware resources. Thus, in this work, we propose an efficient deep learning-based noise reduction method for hearing aid applications. Compared to a previous study, where a fully-connected neural network was used to estimate Wiener filter gains, we focus on using Recurrent Neural Networks (RNNs). Additionally, convolutional layers were integrated. The neural networks were trained to predict real-valued Wiener filter gains to denoise the noisy speech spectrum. Normalizing the input of the neural network is essential. Therefore, various normalization methods were analyzed, allowing low-cost real-time processing. The presented methods were tested and evaluated on unseen noise and speech data. In comparison to the previous study, the computational complexity and the memory requirements of the neural network were reduced by a factor of more than 400, the complexity of the normalization method by a factor of over 200, while even reaching a higher denoising quality.
Deep Learning-based Spectral Noise Reduction for Hearing Aids
Type: MA thesis
Date: August 1, 2019 - February 3, 2020
Supervisors: Hendrik Schröter, Andreas Maier