ONOTE is a unified framework that treats music as a scientifically structured domain of measurable cross-representation correspondences, focusing on omnimodal notation processing centered on sheet music. It introduces a test-only benchmark drawing from diverse musical sources—including staff, Jianpu, and tablature—across varied genres, instruments, and structural conditions, with aligned multimodal derivatives. The framework supports four complementary tasks—score understanding, notation conversion, audio transcription, and symbolic generation—while constructing a provenance-bearing proposition hypergraph from external music-theory materials for evidence retrieval and deterministic validity checks.
By Menghe Ma, Siqing Wei, Yuecheng Xing, Ziyue Zhu, Zhenghong Lin, Yaheng Wang, Fanhong Meng, Peijun Han, Luu Anh Tuan, Haoran Luo
MUUNRiver-Bench is a diagnostic benchmark for music retrieval that uses natural‑language instructions to define relevance for reference‑audio queries. It contains 3,440 tracks across 13 genres and 116 sub‑genres and covers seven tasks such as similar‑music, style‑preserving lyric‑rewriting, cover, and segment retrieval. Experiments with six models in eight configurations show that acoustic encoders favor local identity while text‑aligned encoders favor semantic relations, and that instruction‑aware and audio‑text fusion systems do not consistently outperform their backbones.
By Zhancheng Guo, Congren Dai, Shangda Wu, Jianhuai Hu, Danni Zhao, Xiaobing Li, Maosong Sun
arXiv:2605. 03395v2 Announce Type: replace-cross Abstract: Music popularity prediction has attracted growing research interest, with relevance to artists, platforms, and recommendation systems.
By Jaavid Aktar Husain, Dorien Herremans
Objective evaluation of expressive MIDI piano performances typically relies on attribute statistics such as timing, velocity, and duration of individual notes. However, these methods often disregard dependencies between notes, which poses a potential limitation in assessing the similarity between two sets of performances.
arXiv:2607. 27909v1 Announce Type: cross Abstract: Objective evaluation of expressive MIDI piano performances typically relies on attribute statistics such as timing, velocity, and duration of individual notes.
By Dmitrii Gavrilev, Ilya Borovik, Vladimir Viro
arXiv:2603. 00610v3 Announce Type: replace-cross Abstract: While music generation models have evolved to handle complex multimodal inputs mixing text, lyrics, and reference audio, evaluation mechanisms have lagged behind.
By Yinghao Ma, Haiwen Xia, Hewei Gao, Weixiong Chen, Yuxin Ye, Yuchen Yang, Sungkyun Chang, Mingshuo Ding, Yizhi Li, Ruibin Yuan, Simon Dixon, Emmanouil Benetos