VIOLET: High-Fidelity Violin Synthesis with Techniques and Dynamics

Learning the violin is harder than it looks. Unlike piano keys or guitar frets, the violin neck has no markings at all, so a beginner cannot tell by looking where to place each finger. MusicSynth is an open-source web tool that tries to fix that: user uploads a photo of any violin sheet music (or a digital score file), and the system automatically produces a video showing a violin fingerboard with each note highlighted at the right moment -- no software to install, no manual note entry required. The system connects three existing open-source tools into one pipeline: an optical music recognition (OMR) library reads the notes from the uploaded image, a MusicXML parser extracts timing information from digital scores, and a video renderer draws the fingerboard frame by frame. The only part built from scratch is the lookup table that maps each musical note to a string and finger position on the violin. Tested across 110 public-domain violin scores, MusicSynth correctly identified 91.2,% of notes in clean printed music and assigned the right finger position 99.1,% of the time when given a digital score file. To the author's knowledge, no freely available tool currently turns a sheet music image into an animated violin fingerboard tutorial automatically and in a single browser-based step.

Zero-shot instrument cloning aims to render an arbitrary [Target MIDI] sequence with the acoustic identity of an unseen instrument given only a short [Reference Audio, Reference MIDI] pair. Existing methods rely on pre-trained embeddings (e.g., CLAP) that compress the reference audio into a fixed-length vector, discarding fine-grained acoustic cues essential for faithful timbre reconstruction. We present Anysynth, an embedding-free neural synthesizer based on in-context flow matching. By conditioning a Diffusion Transformer (DiT) directly on the uncompressed reference audio and target MIDI, our model allows self-attention to dynamically retrieve acoustic details at generation time. Experiments show that \tool outperforms embedding-based and auto-regressive baselines in audio quality, timbre similarity, and melody adherence. Notably, the model exhibits prompt-length scaling: longer reference prompts yield steadily better timbre fidelity, a property absent in embedding-based systems. To optimize controllability, we further propose Asymmetric Hierarchical CFG, which structurally decouples MIDI and reference-timbre guidance based on their natural semantic-acoustic dependency. This asymmetric formulation avoids gradient conflicts and improves both note accuracy and timbre fidelity, pushing the boundary of expressive, zero-shot instrument cloning. Demo audios are available at https://anysynth-demo.github.io/

Existing singing voice synthesis systems often require predefined durations, explicit duration prediction, or time-aligned acoustic guidance, which limits their compatibility with practical composition workflows. We propose VocalRender, a score-native system that directly synthesizes singing from lyrics, pitches, symbolic note values, and tempo. It uses an interleaved lyric--note representation and an autoregressive diffusion model to generate continuous acoustic latents while predicting the output length, eliminating the need for explicit duration prediction. Trained on a 2,300-hour singing dataset, VocalRender achieves strong intelligibility, strong melody control, and high speaker similarity across both in-domain and out-of-domain benchmarks. Notably, it outperforms the strongest baseline by $0.42$ points in naturalness CMOS, demonstrating the effectiveness of our proposed score-native architecture.