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

• URL: http://arxiv.org/abs/2606.01686v1
• Date: Mon, 01 Jun 2026 04:51:12 GMT
• Title: HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark
• Abstract summary: We introduce HAIM, a dataset with diverse labels for stages of music production.<n>Our evaluation of state-of-the-art detectors reveals systemic flaws.
• Score: 1.7034813545878587
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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