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Real-world audio recordings are often degraded by factors such as noise,reverberation, and equalization distortion. This paper introduces HiFi-GAN, adeep learning method to transform recorded speech to sound as though it hadbeen recorded in a studio. We use an end-to-end feed-forward WaveNetarchitecture, trained with multi-scale adversarial discriminators in both thetime domain and the time-frequency domain. It relies on the deep featurematching losses of the discriminators to improve the perceptual quality ofenhanced speech. The proposed model generalizes well to new speakers, newspeech content, and new environments. It significantly outperformsstate-of-the-art baseline methods in both objective and subjective experiments.

HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks