Towards Universal Neural Vocoding with a Multi-band Excited WaveNet

TL;DR

This paper presents the Multi-Band Excited WaveNet, a neural vocoder designed to generate high-quality speech and singing voices from diverse mel spectrograms, demonstrating broad domain adaptability with fewer data and parameters.

Contribution

It introduces a multi-band excitation neural vocoder with differentiable components, enabling universal voice synthesis across speakers, languages, and styles with improved efficiency.

Findings

Supports a wide range of voices, languages, and expressivity.

Achieves perceptual quality comparable to state-of-the-art vocoders.

Uses fewer training data and parameters than existing models.

Abstract

This paper introduces the Multi-Band Excited WaveNet a neural vocoder for speaking and singing voices. It aims to advance the state of the art towards an universal neural vocoder, which is a model that can generate voice signals from arbitrary mel spectrograms extracted from voice signals. Following the success of the DDSP model and following the development of the recently proposed excitation vocoders we propose a vocoder structure consisting of multiple specialized DNN that are combined with dedicated signal processing components. All components are implemented as differentiable operators and therefore allow joined optimization of the model parameters. To prove the capacity of the model to reproduce high quality voice signals we evaluate the model on single and multi speaker/singer datasets. We conduct a subjective evaluation demonstrating that the models support a wide range of domain variations (unseen voices, languages, expressivity) achieving perceptive quality that compares with a state of the art universal neural vocoder, however using significantly smaller training datasets and significantly less parameters. We also demonstrate remaining limits of the universality of neural vocoders e.g. the creation of saturated singing voices.