arXiv:2611.07562v1 Announce Type: new Abstract: Perceptual evaluation of generated music is essential for developing text-to-music (TTM) generation systems. Existing automatic music perceptual evaluation methods rely solely on the contrastive language audio pretraining (CLAP) model to extract both audio and text features for predicting human-annotated mean opinion scores (MOS), particularly for overall musical impression (OMI) and textual alignment (TA). However, CLAP is limited in capturing fine-grained musical attributes such as timbre and expressiveness \emph{etc}. To address this, we propose FUSEMOS, a dual-encoder fusion framework that integrates CLAP with MERT, a self-supervised model pretrained on large scale music audio, to better capture both high level semantic alignment and detailed musical characteristics for more accurate OMI and TA estimation. Furthermore, to overcome the limitations of conventional regression losses in measuring human preferred relative ranking of music segments, we design a novel ranking-aware composite loss function. Experiments conducted on the MusicEval benchmark demonstrate that our approach outperforms the existing SOTA methods in both mean squared error and rank correlation metrics by a large margin.