# A Neural Parametric Singing Synthesizer

## TL;DR

This paper introduces a neural singing synthesizer based on a modified WaveNet architecture that models parametric vocoder features, enabling flexible pitch modification, faster training, and generation, with improved robustness and efficiency.

## Contribution

It presents a novel neural singing synthesis model that predicts vocoder features with a multi-stream WaveNet, incorporating regularization for small datasets and a fast generation algorithm.

## Key findings

- Competitive quality in singing synthesis compared to existing methods
- Faster training and generation times due to feature-based modeling
- Effective regularization improves robustness and reduces overfitting

## Abstract

We present a new model for singing synthesis based on a modified version of the WaveNet architecture. Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre. This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times. Our model makes frame-wise predictions using mixture density outputs rather than categorical outputs in order to reduce the required parameter count. As we found overfitting to be an issue with the relatively small datasets used in our experiments, we propose a method to regularize the model and make the autoregressive generation process more robust to prediction errors. Using a simple multi-stream architecture, harmonic, aperiodic and voiced/unvoiced components can all be predicted in a coherent manner. We compare our method to existing parametric statistical and state-of-the-art concatenative methods using quantitative metrics and a listening test. While naive implementations of the autoregressive generation algorithm tend to be inefficient, using a smart algorithm we can greatly speed up the process and obtain a system that's competitive in both speed and quality.