Complex recurrent neural networks for denoising speech signals

Effective denoising of noise-corrupted speech signals remains a challenging problem. Existing solutions typically employ some combination of noise estimation and noise elimination, either by subtraction or by filtering. The estimation of noise and the denoising are generally treated as independent aspects of the problem. In this paper we propose a new neural-network-based approach for de-noising of speech signals. The approach integrates noise estimation and denoising into a single network design, while maintaining many of the aspects of conventional noise estimation and signal denoising through a recurrent gated structure. The network thus operates as a single integrated process that can be trained to jointly estimate noise and denoise the speech signal with minimal artifacts. Noise reduction experiments on noisy speech, both with digitally added synthetic noise and real car noise, show that the proposed algorithm can recover much of the degradation caused by the noise.