{"ID":2894655,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09904","arxiv_id":"2507.09904","title":"ASTAR-NTU solution to AudioMOS Challenge 2025 Track1","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.","short_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 winnin...","url_abs":"https://arxiv.org/abs/2507.09904","url_pdf":"https://arxiv.org/pdf/2507.09904v1","authors":"[\"Fabian Ritter-Gutierrez\",\"Yi-Cheng Lin\",\"Jui-Chiang Wei\",\"Jeremy H. M. Wong\",\"Nancy F. Chen\",\"Hung-yi Lee\"]","published":"2025-07-14T04:18:15Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"eess.AS\"]","methods":"[]","has_code":false}