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arXiv · 2507.09904

ASTAR-NTU solution to AudioMOS Challenge 2025 Track1

Fabian Ritter-Gutierrez·Yi-Cheng Lin·Jui-Chiang Wei·Jeremy H. M. Wong·Nancy F. Chen·Hung-yi Lee
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Abstract

Evaluation of text-to-music systems is constrained by the cost and availability of collecting experts for assessment. AudioMOS 2025 Challenge track 1 is created to automatically predict music impression (MI) as well as text alignment (TA) between the prompt and the generated musical piece. This paper reports our winning system, which uses a dual-branch architecture with pre-trained MuQ and RoBERTa models as audio and text encoders. A cross-attention mechanism fuses the audio and text representations. For training, we reframe the MI and TA prediction as a classification task. To incorporate the ordinal nature of MOS scores, one-hot labels are converted to a soft distribution using a Gaussian kernel. On the official test set, a single model trained with this method achieves a system-level Spearman's Rank Correlation Coefficient (SRCC) of 0.991 for MI and 0.952 for TA, corresponding to a relative improvement of 21.21\% in MI SRCC and 31.47\% in TA SRCC over the challenge baseline.

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Fabian Ritter-Gutierrez, Yi-Cheng Lin, Jui-Chiang Wei, Jeremy H. M. Wong, Nancy F. Chen, Hung-yi Lee. 2025-07-14. ASTAR-NTU solution to AudioMOS Challenge 2025 Track1. https://arxiv.org/abs/2507.09904

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