High-Fidelity Music Vocoder using Neural Audio Codecs

Luca A. Lanzendörfer; Florian Grötschla; Michael Ungersböck; Roger Wattenhofer

ICASSP 2025accepted0 citations

High-Fidelity Music Vocoder using Neural Audio Codecs

Luca A. Lanzendörfer, Florian Grötschla, Michael Ungersböck, Roger Wattenhofer

Abstract

While neural vocoders have made significant progress in high-fidelity speech synthesis, their application on polyphonic music has remained underexplored. In this work, we propose DisCoder, a neural vocoder that leverages a generative adversarial encoder-decoder architecture informed by a neural audio codec to reconstruct high-fidelity 44.1 kHz audio from mel spectrograms. Our approach first transforms the mel spectrogram into a lower-dimensional representation aligned with the Descript Audio Codec (DAC) latent space before reconstructing it to an audio signal using a fine-tuned DAC decoder. DisCoder achieves state-of-the-art performance in music synthesis on several objective metrics and in a MUSHRA listening study. Our approach also shows competitive performance in speech synthesis, highlighting its potential as a universal vocoder.

BibTeX

@inproceedings{icassp2025_highfidelitymusi,
  title = {High-Fidelity Music Vocoder using Neural Audio Codecs},
  author = {Luca A. Lanzendörfer and Florian Grötschla and Michael Ungersböck and Roger Wattenhofer},
  booktitle = {ICASSP 2025},
  year = {2025}
}