HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark
As generative platforms such as Suno and Udio reach human-grade audio quality, the scope of AI's utility has expanded across the entire music production workflow. Beyond simple track generation, these advancements have catalyzed the adoption of AI-driven methodologies in diverse forms. These include vocal synthesis, arrangement, and professional mastering. However, current detection research remains largely confined to a binary `AI-or-human' paradigm. It fails to reflect the realities of contemporary music production workflows. In real-world production, AI tools are increasingly used to refine or master human-produced tracks, and human engineers likewise post-process AI-generated material...
| Authors | Seonghyeon Go, Yumin Kim |
|---|---|
| Primary category | cs.SD |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
Models and datasets referencing this paper
- mippia/HAIMDataset
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