Reliability of Uncertainty-Aware Listener-Rating Prediction for Piano Performances Under Distribution Shifts
Year: 2026
Type: article
Abstract: Models can predict listener ratings of piano performances, but their reliability under distribution change remains unclear. We study 19 perceptual and descriptive attributes in PercePiano using valid-only ratings, frozen MERT and MuQ representations, grouped out-of-fold evaluation, and two synthesis pipelines. The primary comparison excludes two reference-rendering categories and retrains composition-LOPO and human-performer LOPO models on the same 1072 human-performance segments. Rank association with mean listener ratings was markedly lower for held-out composition IDs than for held-out human-performer IDs (MERT: 0.203 vs. 0.599; MuQ: 0.206 vs. 0.629). Categorical rating distributions aligned more closely with observed listener dispersion than ensemble uncertainty under proxy-grouped evaluation, although this pattern did not transfer consistently across compositions. Paired renderer tests found positive transfer penalties in all four encoder–source combinations. Score-relative late fusion, conformal calibration, and labeled target-composition adaptation improved numerical error, empirical coverage, and target-specific prediction, respectively. Interpretation is limited by finite-panel disagreement and the small NeuroPiano comparison. These experiments characterize the reliability boundary of listener-rating prediction in this corpus and motivate composition-aware evaluation on broader acoustic-piano data.
Source: Electronics
Authors: Xueying Bai, Nanmu Hui, Yanping Li, Xiaowei Han
Institution: Shenyang University
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7204470648