Modern speech content creation tasks such as podcasts, video voice-overs, and audio books require studio-quality audio with full bandwidth and balanced equalization (EQ). These goals pose a challenge for conventional speech enhancement methods, which typically focus on removing significant acoustic degradation such as noise and reverb so as to improve speech clarity and intelligibility. We present HiFi-GAN-2, a waveform-to-waveform enhancement method that improves the quality of real-world consumer-grade recordings, with moderate noise, reverb and EQ distortion, to sound like studio recordings. HiFi-GAN-2 has three components. First, given a noisy reverberant recording as input, a recurrent network predicts the acoustic features (MFCCs) of a clean signal. Second, given the same noisy input, and conditioned on the MFCCs output by the first network, a feed-forward WaveNet (modeled via multi-domain multi-scale adversarial training) generates a clean 16kHz signal. Third, a pre-trained bandwidth extension network generates the final 48kHz studio-quality signal from the 16kHz output of the second network. The complete pipeline is trained via simulation of noise, reverb and EQ added to studio-quality speech. Objective and subjective evaluations show that the proposed method outperforms state-of-the-art baselines on both conventional denoising as well as joint dereverberation and denoising tasks. Listening tests also show that our method achieves close to studio quality on real-world speech content (TED Talks and the VoxCeleb dataset).

@inproceedings{Su:2021:HSS,
   author = "Jiaqi Su and Zeyu Jin and Adam Finkelstein",
   title = "{HiFi}-{GAN}-2: Studio-quality Speech Enhancement via Generative
      Adversarial Networks Conditioned on Acoustic Features",
   booktitle = "WASPAA 2021",
   year = "2021",
   month = oct
}