音乐转录需同时评估乐谱准确性和演奏还原度，两者互补。
A Dual Evaluation for Music Transcription
- 分两维度评估：乐谱相似性与演奏还原度
- CLEWS指标既最贴近人评又计算成本最低
- 不同系统在两指标上表现差异大，适合不同需求
自动音乐转录系统生成可读可播放的乐谱。我们提出应分别评估乐谱与参考谱的相似性、以及演奏与原表演的相似性。研究采用光学乐谱识别领域的乐谱相似性指标，及多种经超过100名参与者、230段钢琴录音（涵盖23部作品、30位演奏者、6位作曲家）验证的演奏相似性方法。发现与人类判断相关性最高的播放相似性指标CLEWS，同时也是运行成本最低的。在24种流水线（8个音频转MIDI模型+3个MIDI转乐谱转换器）中，两个评价维度偏好不同系统，且后者组件决定整体偏好。当加入新端到端系统Rubato后，其乐谱相似性显著提升，演奏相似性仍保持竞争力但非最优。该双维度互补性依然成立。
原文摘要 · Abstract (English)
Automatic music transcription systems produce sheet music that can be read and played back. We argue that these two targets call for complementary evaluations of notation similarity to a reference score and playback similarity to the original performance, respectively. Our study considers notation similarity metrics from the optical music recognition literature and a wide range of playback-similarity methods validated through a listening study across over 100 participants and 230 piano recordings covering 23 works, 30 performers, and six composers. We find, fortuitously, that the playback similarity metric that correlates best with human judgments, CLEWS, is also the cheapest to run. We also find that the two evaluation dimensions favor different systems among a collection of 24 pipelines formed by pairing eight audio-to-MIDI models with three MIDI-to-score converters, with the latter component systematically determining the favored objective. The complementarity between metrics also holds when adding to the pool Rubato, a new end-to-end system that offers substantially improved notation similarity while remaining competitive, though not the best, on playback similarity.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。