Neural Audio Codec Bottlenecks and Perceptual Quality in GAN Vocoders for Polyphonic Music

Abstract

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. Di

Research Question

What is the effect of neural audio codec bottlenecks on the perceptual quality scores of generative adversarial vocoders trained on polyphonic music spectrograms?

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