# DDSP-based Neural Waveform Synthesis of Polyphonic Guitar Performance from String-wise MIDI Input

arXiv: https://arxiv.org/abs/2309.07658 · HTML: https://mlsanity.com/paper/2309.07658/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2023.

## Built on by (most cited first, 3 of 3)

- [Sines, Transient, Noise Neural Modeling of Piano Notes](https://mlsanity.com/paper/2409.06513/) (2024)
- [Exploring Procedural Data Generation for Automatic Acoustic Guitar Fingerpicking Transcription](https://mlsanity.com/paper/2508.07987/) (2025)
- [VIOLET: High-Fidelity Violin Synthesis with Techniques and Dynamics](https://mlsanity.com/paper/2608.07944/) (2026)

## Abstract (arXiv)

We explore the use of neural synthesis for acoustic guitar from string-wise MIDI input. We propose four different systems and compare them with both objective metrics and subjective evaluation against natural audio and a sample-based baseline. We iteratively develop these four systems by making various considerations on the architecture and intermediate tasks, such as predicting pitch and loudness control features. We find that formulating the control feature prediction task as a classification task rather than a regression task yields better results. Furthermore, we find that our simplest proposed system, which directly predicts synthesis parameters from MIDI input performs the best out of the four proposed systems. Audio examples are available at https://erl-j.github.io/neural-guitar-web-supplement.