Publications

HiFi-GAN-2: Studio-quality speech enhancement via generative adversarial networks conditioned on acoustic features

IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

Published October 17, 2021

Jiaqi Su, Zeyu Jin, Adam Finkelstein

Modern speech content creation tasks such as podcasts, video voice-overs, and audiobooks 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).


Research Areas:  AI & Machine Learning Audio