Noise Reduction in Hearing-Aid Processors: Traditional Methods vs. Neural Networks

Many deep neural networks (DNNs) have been applied lately in the field of speech enhancement. One particular subfield, where DNNs have shifted the boundaries of what is considered possible, is noise reduction, where the degrading effects of sounds interfering with speech are minimized. This is especially relevant for hearing impaired listeners, as their ability to understand speech in noisy circumstances is reduced. In contrast to traditional methods, which are known to improve speech quality, DNNs promise to also improve speech intelligibility. Due to the high computational complexity, DNNs have not yet been deployed on a hearing aid processor, constrained by frequencies up to 50 MHz and memory up to 2 MB. In this work we deploy a convolutional neural network (CNN) trained for noise reduction to a hearing-aid system-on-chip (SoC) developed at our institute. Real time capability is achieved by thorough optimization of the C -Code, leading to a speed up by a factor of 88 for the inference relevant layers when compared to a naïve C-Code implementation. The CNN approach is compared to an implementation of a traditional noise reduction method regarding their speech enhancement performance on white and complex noise and their computational cost. While both methods improve the speech quality measured with Perceptual Evaluation of Speech Quality (PESQ), only the CNN achieves a Short-Time Objective Intelligibility (STOI) improvement of 0.077 for complex noise. On the other hand, the CNN has a higher processor utilization of 60.1% compared to 23.5% for the traditional approach. Nonetheless, both methods are real time capable and consume only 3.3 mW for the CNN and 1.78 mW for the traditional approach, respectively.