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.

翻译：学习小提琴比表面看上去更难。与钢琴键盘或吉他品格不同，小提琴指板上没有任何标记，因此初学者无法通过视觉判断每个手指的按放位置。MusicSynth 是一款开源网络工具，旨在解决这一问题：用户上传任意小提琴乐谱照片（或数字乐谱文件），系统即可自动生成一段视频，在该视频中，每一音符均在小提琴指板的正确位置高亮显示——无需安装软件，也无需手动输入音符。该系统将三种现有开源工具整合至单一流水线中：光学乐谱识别（OMR）库从上传图像中读取音符，MusicXML解析器从数字乐谱中提取时序信息，视频渲染器则逐帧绘制指板。唯一从头构建的部分是将每个音符映射至小提琴各弦和手指位置的查找表。在110首公共领域小提琴乐谱的测试中，MusicSynth 对清晰印刷乐谱的音符识别准确率为91.2%，在给定数字乐谱文件时指法分配正确率达99.1%。据作者所知，目前尚无免费工具能够仅通过浏览器且自动完成从乐谱图像到动画小提琴指板教程的转换。