DeRA-MOS: Optimizing Text-to-Music Evaluation via Decoupled Listwise Ranking and Modality Alignment
arXiv:2606.10010v1 Announce Type: cross Abstract: Evaluating text-to-music (TTM) systems remains expensive because music impression (MI) and text alignment (TA) scores rely on human mean opinion scores (MOS). Most automatic MOS estimators are trained with point-wise regression or distributional classification. These objectives do not directly optimize rank-based metrics and provide weak geometric constraints for cross-modal coherence. To address these gaps, we propose DeRA-MOS, a decoupled optimization framework for TTM evaluation. For MI, we introduce a batch-aware listwise ranking loss that
The proliferation of advanced text-to-music AI systems necessitates more efficient and accurate evaluation methods to accelerate development and deployment.
Improved TTM evaluation can lower the cost and time barrier for developing creative AI applications, impacting entertainment, education, and content creation sectors.
The proposed DeRA-MOS framework offers a more robust, automatable, and cost-effective way to assess AI-generated music, moving beyond expensive human evaluation.
- · AI developers (music generation)
- · Content creators
- · Entertainment industry
- · AI evaluation companies
- · Traditional human evaluators (MOS)
- · Companies reliant on outdated evaluation methods
More rapid iteration and improvement in text-to-music AI models due to efficient evaluation.
Increased adoption and commercialization of AI-generated music across various industries and platforms.
The development of entirely new forms of media and artistic expression enabled by highly capable and accessible music generation AI.
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Read at arXiv cs.AI