# VIOLET: High-Fidelity Violin Synthesis with Techniques and Dynamics

arXiv: https://arxiv.org/abs/2608.07944 · HTML: https://mlsanity.com/paper/2608.07944/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 25 of its 47 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 25 of 25)

- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [High-Fidelity Audio Compression with Improved RVQGAN](https://mlsanity.com/paper/2306.06546/) (2023)
- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Stable Audio Open](https://mlsanity.com/paper/2407.14358/) (2024)
- [Fast Timing-Conditioned Latent Audio Diffusion](https://mlsanity.com/paper/2402.04825/) (2024)
- [Audiobox: Unified Audio Generation with Natural Language Prompts](https://mlsanity.com/paper/2312.15821/) (2023)
- [Adapting Frechet Audio Distance for Generative Music Evaluation](https://mlsanity.com/paper/2311.01616/) (2023)
- [TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization](https://mlsanity.com/paper/2412.21037/) (2024)
- [MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling](https://mlsanity.com/paper/2112.09312/) (2021)
- [FlashAudio: Rectified Flows for Fast and High-Fidelity Text-to-Audio Generation](https://mlsanity.com/paper/2410.12266/) (2024)
- [TechSinger: Technique Controllable Multilingual Singing Voice Synthesis via Flow Matching](https://mlsanity.com/paper/2502.12572/) (2025)
- [ControlSpeech: Towards Simultaneous and Independent Zero-shot Speaker Cloning and Zero-shot Language Style Control](https://mlsanity.com/paper/2406.01205/) (2024)
- [Deep Performer: Score-to-Audio Music Performance Synthesis](https://mlsanity.com/paper/2202.06034/) (2022)
- [PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network](https://mlsanity.com/paper/1811.04357/) (2018)
- [MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners](https://mlsanity.com/paper/2506.18729/) (2025)
- [Spontaneous Style Text-to-Speech Synthesis with Controllable Spontaneous Behaviors Based on Language Models](https://mlsanity.com/paper/2407.13509/) (2024)
- [MIDI-VALLE: Improving Expressive Piano Performance Synthesis Through Neural Codec Language Modelling](https://mlsanity.com/paper/2507.08530/) (2025)
- [DDSP-based Neural Waveform Synthesis of Polyphonic Guitar Performance from String-wise MIDI Input](https://mlsanity.com/paper/2309.07658/) (2023)
- [MOSA: Music Motion with Semantic Annotation Dataset for Cross-Modal Music Processing](https://mlsanity.com/paper/2406.06375/) (2024)
- [ViolinDiff: Enhancing Expressive Violin Synthesis with Pitch Bend Conditioning](https://mlsanity.com/paper/2409.12477/) (2024)
- [VioPTT: Violin Technique-Aware Transcription from Synthetic Data Augmentation](https://mlsanity.com/paper/2509.23759/) (2025)

## Abstract (arXiv)

Neural synthesis for musical instruments has the potential to revolutionize current practices that use concatenative synthesis and a sample library. However, most research focused on piano synthesis and expressive performance generation; little work has been done on continuously articulated instruments like the violin, let alone rendering them with playing techniques and dynamics. We present VIOLET, a latent-diffusion framework for controllable violin synthesis, which uses a Diffusion Transformer (DiT) with rectified flow to synthesize high-fidelity audio from MIDI notes, playing techniques, and continuous dynamics. To train VIOLET, in addition to using a few existing datasets, we curate a new dataset named CSV-TD, which contains 39 h of 48 kHz synthetic audio and time-aligned annotations of MIDI notes, note-level techniques, and continuous dynamics curves. Objective and subjective evaluations show that VIOLET synthesizes violin performances with high technique adherence, accurate pitch and timing alignment, and good dynamics control. It outperforms the current state-of-the-art neural violin synthesis system and approaches a top commercial virtual instrument in terms of technique clarity, naturalness, and dynamics following.