SERGAN: Speech Enhancement using Relativistic Generative Adversarial Networks with Gradient Penalty


Popular neural network-based speech enhancement systems operate on the magnitude spectrogram and ignore the phase mismatch between the noisy and clean speech signals. Recently, conditional generative adversarial networks (cGANs) have shown promise in addressing the phase mismatch problem by directly mapping the raw noisy speech waveform to the underlying clean speech signal. However, stabilizing and training cGAN systems is difficult and they still fall short of the performance achieved by spectral enhancement approaches. This paper introduces relativistic GANs with a relativistic cost function at its discriminator and gradient penalty to improve time-domain speech enhancement. Simulation results show that relativistic discriminators provide a more stable training of cGANs and yield a better generator network for improved speech enhancement performance.

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK
Deepak Baby
Deepak Baby
Applied Scientist

My research interests include speech recognition, enhancement and deep learning.

Sarah Verhulst
Sarah Verhulst
Associate Professor

Associate Professor at Ghent University, Belgium

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