# HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark

arXiv: https://arxiv.org/abs/2606.01686 · HTML: https://mlsanity.com/paper/2606.01686/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 17 of 17)

- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization](https://mlsanity.com/paper/2501.01108/) (2025)
- [ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation](https://mlsanity.com/paper/2602.00744/) (2026)
- [SingFake: Singing Voice Deepfake Detection](https://mlsanity.com/paper/2309.07525/) (2023)
- [SONICS: Synthetic Or Not -- Identifying Counterfeit Songs](https://mlsanity.com/paper/2408.14080/) (2024)
- [Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects](https://mlsanity.com/paper/2211.02247/) (2022)
- [Benchmarking Music Generation Models and Metrics via Human Preference Studies](https://mlsanity.com/paper/2506.19085/) (2025)
- [A Fourier Explanation of AI-music Artifacts](https://mlsanity.com/paper/2506.19108/) (2025)
- [Detecting music deepfakes is easy but actually hard](https://mlsanity.com/paper/2405.04181/) (2024)
- [SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering](https://mlsanity.com/paper/2508.03448/) (2025)
- [K-pop Lyric Translation: Dataset, Analysis, and Neural-Modelling](https://mlsanity.com/paper/2309.11093/) (2023)
- [AI-Generated Music Detection and its Challenges](https://mlsanity.com/paper/2501.10111/) (2025)
- [From Generation to Attribution: Music AI Agent Architectures for the Post-Streaming Era](https://mlsanity.com/paper/2510.20276/) (2025)
- [AI-Generated Song Detection via Lyrics Transcripts](https://mlsanity.com/paper/2506.18488/) (2025)
- [Melody or Machine: Detecting Synthetic Music with Dual-Stream Contrastive Learning](https://mlsanity.com/paper/2512.00621/) (2025)
- [Fusion Segment Transformer: Bi-Directional Attention Guided Fusion Network for AI-Generated Music Detection](https://mlsanity.com/paper/2601.13647/) (2026)

## Abstract (arXiv)

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 to ensure professional quality. Moreover, users often employ adversarial tactics to bypass AI detectors, such as applying human mastering to AI-generated tracks. This creates a grey area that a simple binary classification fails to capture. In this paper, we define and investigate ``AI Music Tracking'': the challenge of identifying specific AI integration across the multifaceted spectrum of music production. To this end, we introduce HAIM, a dataset with diverse labels for stages of music production. It is designed to isolate stages of AI intervention, including hybrid production and agent-level tracking. Our evaluation of state-of-the-art detectors reveals systemic flaws. By releasing HAIM, we propose a new benchmark that shifts the field beyond binary classification toward a granular, structured evaluation of AI music.