Speech Denoising and Compensation for Hearing Aids Using an FTCRN-Based Metric GAN

Hearing aids aims to improve speech intelligibility for hearing impaired patients to levels comparable to those for normal hearing listeners. However, the interference of environmental noises greatly increase the difficulty of hearing loss compensation. Most related research only focuses on one aspect of noise reduction and hearing loss compensation. In this letter, we propose a metric generative adversarial framework based on a frequency-time convolution recurrent network for joint noise reduction and hearing loss compensation. The audiogram is extended along the frequency axis to form embedded features. A metric discriminator is introduced and the optimization of the generator is guided by an evaluation score related to hearing loss compensation. Additional perceptual-based losses are set to stabilize optimization. Experimental results show that the proposed method can better reduce noise and compensate for hearing loss compared with other algorithms.

Automated summary

In this letter, a metric generative adversarial framework based on a frequency-time convolution recurrent network is proposed for joint noise reduction and hearing loss compensation. Experimental results show that this method can better reduce noise and compensate for hearing loss compared with other algorithms.