MusicHiFi: Fast High-Fidelity Stereo Vocoding
Authors: name: Ge Zhu, name: Juan-Pablo Caceres, name: Zhiyao Duan, name: Nicholas J. Bryan

Diffusion-based audio and music generation models commonly generate music by
constructing an image representation of audio (e.g., a mel-spectrogram) and
then converting it to audio using a phase reconstruction model or vocoder.
Typical vocoders, however, produce monophonic audio at lower resolutions (e.g.,
16-24 kHz), which limits their effectiveness. We propose MusicHiFi -- an
efficient high-fidelity stereophonic vocoder. Our method employs a cascade of
three generative adversarial networks (GANs) that convert low-resolution
mel-spectrograms to audio, upsamples to high-resolution audio via bandwidth
expansion, and upmixes to stereophonic audio. Compared to previous work, we
propose 1) a unified GAN-based generator and discriminator architecture and
training procedure for each stage of our cascade, 2) a new fast, near
downsampling-compatible bandwidth extension module, and 3) a new fast
downmix-compatible mono-to-stereo upmixer that ensures the preservation of
monophonic content in the output. We evaluate our approach using both objective
and subjective listening tests and find our approach yields comparable or
better audio quality, better spatialization control, and significantly faster
inference speed compared to past work. Sound examples are at
https://MusicHiFi.github.io/web/.