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https://nanless.github.io/audio-paper-digest-blog/posts/2026-04-23-onote-benchmarking-omnimodal-notation-processing/ 1. **问题**：当前多模态大模型在音乐符号处理（Omnimodal Notation Processing, ONP）领域存在严重缺陷：研究碎片化、模型存在严重的符号偏差（偏向五线谱）、且普遍依赖不可靠的“LLM-as-a-Judge”评估方法，掩盖了模型在音乐理论推理上的系统性失败。 2. https://nanless.github.io/audio-paper-digest-blog/posts/2026-04-21-coexisting-tempo-traditions-in-beethovens-piano/ Tue, 21 Apr 2026 00:00:00 +0000 https://nanless.github.io/audio-paper-digest-blog/posts/2026-04-21-coexisting-tempo-traditions-in-beethovens-piano/ 本文旨在挑战音乐表演实证研究中普遍使用的单一回归分析模型，该模型常将历史速度变化描绘为一个单向、统一的过程。作者提出，这种模型掩盖了多种演奏传统并存的事实。研究通过对贝多芬五首钢琴与大提琴奏鸣曲（Op. 5, 69, 102）在1930-2012年间超过一百个乐章录音的逐小节速度数据进行K-mean https://nanless.github.io/audio-paper-digest-blog/posts/2026-04-20-tinymu-a-compact-audio-language-model-for-music/ Mon, 20 Apr 2026 00:00:00 +0000 https://nanless.github.io/audio-paper-digest-blog/posts/2026-04-20-tinymu-a-compact-audio-language-model-for-music/ 本文针对现有大型音频语言模型（LALM）参数庞大（数十亿级）、训练推理成本高、难以部署在边缘设备的问题，提出了 TinyMU——一个仅有 229M 参数的紧凑音乐语言模型。为此，作者构建了 MusicSkills-3.5M 数据集，包含 350 万个涵盖多选、二元判断和开放式格式的音乐问答样本，结合