SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment
Recent advancements in Text-to-Song generation have enabled realistic musical content production, yet existing evaluation benchmarks lack the professional granularity to capture multi-dimensional aesthetic nuances. In this paper, we propose SongBench, a specialized framework for fine-grained song assessment across seven key dimensions: Vocal, Instrument, Melody, Structure, Arrangement, Mixing, and Musicality. Utilizing this framework, we construct an expert-annotated database comprising 11,717 samples from state-of-the-art models, labeled by music professionals. Extensive experimental results demonstrate that SongBench achieves high correlation with expert ratings. By revealing fine-grain...
| Authors | Dapeng Wu, Shun Lei, Wei Tan et al. |
|---|---|
| Primary category | eess.AS |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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