# VioTech: Frame-Level Violin Playing Technique Detection with Multi-Scale Mixture-of-Experts

**Authors:** Ting-Kang Wang, Vincent K. M. Cheung, Nan-Tien Lai, Li Su  
**Lab:** Institute of Information Science, Academia Sinica · **Published:** 2 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.02680) · [PDF](https://arxiv.org/pdf/2611.02680) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.02680.html)

## Abstract

Playing technique is crucial for expressive violin performance, yet few violin technique detection models exist. Current systems mainly rely on note-level pipelines that first estimate note boundaries and then classify techniques. This is especially problematic for bowed string instruments such as the violin, where soft onsets and continuous bow-string interaction render note segmentation unreliable. Here, we propose VioTech, a frame-level violin technique detection framework that predicts techniques directly at 10 ms resolution without explicit note segmentation. To handle the large temporal-scale variation among techniques, VioTech combines a frozen MERT encoder with a multi-scale Mixture-of-Experts (MoE) architecture and task-specific routing for tonal, articulation, and legato-related predictions. We also introduce VioTech120, the first manually annotated dataset of violin playing techniques constructed from real solo performance recordings, as well as an accompanying note-labeling user interface. Results demonstrate that VioTech achieved consistent improvements over prior methods and significantly enhanced cross-dataset generalization, establishing a new state-of-the-art in violin technique-aware transcription.

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