跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文,不再错过。

覆盖计算机全学科,以 AI 为快捷入口。关注方向、作者和关键词,把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+7272新增论文
+6090其中 AI 相关
+125已有论文修订
+122新增 AI 导读

arXiv 最近完成扫描:2026-11-05 17:24 · 全站最近入库:2026-11-05 12:13各信源进度 →

统计口径与数据新鲜度

统计 2026-11-04 18:16 — 2026-11-05 18:16(北京时间) · 点击数字查看对应简报;AI 相关包含在新增论文内。

最近完成检查:2026-11-05 17:24最近发现新增版本:2026-11-05 12:13库内论文最新更新时间:2026-11-05 02:59

每分钟刷新已保存的数据。空检查不代表采集异常;新收录可能包含历史论文。AI 导读按新增版本计数。

15,949 已收录论文13,765 AI 相关3,571 有修订112 HF 社区发现

浏览论文库

时间范围跟随排序:版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常,查看状态 →

找到 1 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-05 17:22(北京时间)
新论文 · v1arXiv:2610.38545

TSM diff: A new Parallel-Tree-Traversal Algorithm for the Comparison of Music Score Files

Xiaobo Wang, Florent Jacquemard

Music score files are more difficult to compare than their text-based equivalents, as their content is more complex and organised into hierarchical structures. This explains the lack of tools for processing music scores similar to the widely used old Unix diff command. We propose a new algorithm and tool for comparing digital music scores in various encodings, such as MusicXML or MEI, based on a graph-structured abstract representation called Tree Score Model (TSM). Given two scores, after conversion into TSM, our procedure calculates jointly: A list of differences, localised by time positions in the input scores; A time distance, cumulated duration of time-spans where scores differ; An edit distance, called surface distance, based on notational attributes not related to time. The two later measures are normalised and combined into a similarity distance between scores. Our procedure has been implemented in C++ and is available as a Python module. Our first experiments have shown a very good efficiency even on challenging data. As a proof of concept, we propose two demos based on different versions of some Mozart's piano sonata, and transcriptions of monophonic soli on the same jazz standards by different performers. https://tsm-diff-lbd-7a7e2a.gitlab.io/demo.html

尚无 AI 解读,展示原文摘要

cs.MM
arXiv ↗PDF ↗