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...

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Bibliographic Details
Published inProceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 106 - 110
Main Authors Baby, Deepak, Verhulst, Sarah
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
Convolutional neural networks
Cost function
Generative adversarial networks
Generators
Noise measurement
relativistic GAN
Simulation
Spectrogram
Speech enhancement
Time-domain analysis
Training
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8683799