# Singing Synthesis: with a little help from my attention

arXiv: https://arxiv.org/abs/1912.05881 · HTML: https://mlsanity.com/paper/1912.05881/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 8 of 8)

- [Sinsy: A Deep Neural Network-Based Singing Voice Synthesis System](https://mlsanity.com/paper/2108.02776/) (2021)
- [Singing-Tacotron: Global duration control attention and dynamic filter for End-to-end singing voice synthesis](https://mlsanity.com/paper/2202.07907/) (2022)
- [KaraSinger: Score-Free Singing Voice Synthesis with VQ-VAE using Mel-spectrograms](https://mlsanity.com/paper/2110.04005/) (2021)
- [Improved Prosodic Clustering for Multispeaker and Speaker-independent Phoneme-level Prosody Control](https://mlsanity.com/paper/2111.10168/) (2021)
- [Controllable speech synthesis by learning discrete phoneme-level prosodic representations](https://mlsanity.com/paper/2211.16307/) (2022)
- [Rapping-Singing Voice Synthesis based on Phoneme-level Prosody Control](https://mlsanity.com/paper/2111.09146/) (2021)
- [Karaoker: Alignment-free singing voice synthesis with speech training data](https://mlsanity.com/paper/2204.04127/) (2022)
- [Singing Voice Synthesis Based on a Musical Note Position-Aware Attention Mechanism](https://mlsanity.com/paper/2212.13703/) (2022)

## Abstract (arXiv)

We present UTACO, a singing synthesis model based on an attention-based sequence-to-sequence mechanism and a vocoder based on dilated causal convolutions. These two classes of models have significantly affected the field of text-to-speech, but have never been thoroughly applied to the task of singing synthesis. UTACO demonstrates that attention can be successfully applied to the singing synthesis field and improves naturalness over the state of the art. The system requires considerably less explicit modelling of voice features such as F0 patterns, vibratos, and note and phoneme durations, than previous models in the literature. Despite this, it shows a strong improvement in naturalness with respect to previous neural singing synthesis models. The model does not require any durations or pitch patterns as inputs, and learns to insert vibrato autonomously according to the musical context. However, we observe that, by completely dispensing with any explicit duration modelling it becomes harder to obtain the fine control of timing needed to exactly match the tempo of a song.