SongBench: New Benchmark for Multi-Aspect Song Quality Assessment
Researchers have introduced SongBench, a framework for evaluating text-to-song generation across seven dimensions: Vocal, Instrument, Melody, Structure, Arrangement, Mixing, and Musicality. An expert-annotated database of 11,717 samples from state-of-the-art models was created, labeled by music professionals. Experiments show SongBench correlates highly with expert ratings, revealing performance gaps to guide development toward more professional song generation.
Key facts
- SongBench evaluates song quality across seven dimensions: Vocal, Instrument, Melody, Structure, Arrangement, Mixing, and Musicality.
- The database contains 11,717 samples from state-of-the-art models.
- Samples were labeled by music professionals.
- SongBench achieves high correlation with expert ratings.
- The benchmark is designed to diagnose fine-grained performance gaps.
- It aims to steer development toward more professional and musically coherent song generation.
- The work addresses limitations in existing evaluation benchmarks for text-to-song generation.
- SongBench is a specialized framework for fine-grained song assessment.
Entities
Institutions
- arXiv