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. 14537v1 Announce Type: cross Abstract: Rich internal representations of musical structure are essential for music understanding tasks such as machine-assisted music co-writing, yet self-supervised approaches for symbolic music representation remain underexplored, particularly those that encode the hierarchical multiscale nature of musical structures.
By Scott H. Hawley
arXiv:2512. 02652v2 Announce Type: replace-cross Abstract: Existing methods for expressive music performance rendering, a conditional generation task that aims to generate a human-like performance from a symbolic score, rely on supervised learning over small labeled datasets, which limits scaling of both data volume and model size, despite the availability of vast unlabeled music, as in vision and language.
By Hong-Jie You, Jie-Jing Shao, Xiao-Wen Yang, Lin-Han Jia, Lan-Zhe Guo, Yu-Feng Li
arXiv:2608. 03920v1 Announce Type: cross Abstract: Humans recognize a musical passage even when it is shifted in time or transposed in pitch, indicating a notion of equivariance in the representation space.
By Zixun Guo, Simon Dixon
The paper investigates whether joint language‑audio embedding models encode human perceptual timbre semantics. It evaluates several state‑of‑the‑art models, finding that LAION‑CLAP aligns best with human‑perceived timbre across instrumental sounds and descriptor‑conditioned audio effects, yet the overall alignment remains limited. The study also notes that reverb‑induced timbre semantics are more consistently captured than equalization‑induced ones.
By Qixin Deng, Bryan Pardo, Thrasyvoulos N Pappas
arXiv:2607. 06929v1 Announce Type: cross Abstract: Music aesthetic assessment is a challenging yet underexplored problem, requiring models to capture fine-grained, multi-dimensional human perceptual judgments.
By Sirui Zhang, Tianle Wang, Xinyi Tong, Peiyang Yu, Jishang Chen, Liangke Zhao, Haoxin Zhang, Duo Xu, Xin Jin, Feng Yu, Songchun Zhu