HAIM: 用于AI音乐制作跟踪基准的人机音乐数据集
HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark
AI总结：
针对当前AI音乐检测局限于二元分类的不足，提出HAIM数据集，通过多阶段标签定义“AI音乐跟踪”任务，评估现有检测器缺陷，推动向细粒度结构化评估转变。
摘要：
随着Suno和Udio等生成平台达到人类级音频质量，AI的实用性已扩展到整个音乐制作流程。除了简单的音轨生成，这些进步催生了AI驱动方法在各种形式中的应用，包括声音合成、编曲和专业母带处理。然而，当前的检测研究仍主要局限于二元“AI或人类”范式，未能反映当代音乐制作流程的现实。在真实制作中，AI工具越来越多地被用于优化或母带处理人类制作的音轨，而人类工程师同样对AI生成的材料进行后处理以确保专业质量。此外，用户经常采用对抗策略绕过AI检测器，例如对AI生成的音轨应用人类母带处理。这创造了一个简单的二元分类无法捕捉的灰色地带。在本文中，我们定义并研究“AI音乐跟踪”：在音乐制作的多面光谱中识别特定AI集成的挑战。为此，我们引入HAIM，一个具有音乐制作阶段多样化标签的数据集。它旨在隔离AI干预的阶段，包括混合制作和代理级跟踪。我们对最先进检测器的评估揭示了系统性缺陷。通过发布HAIM，我们提出了一个新的基准，将领域从二元分类转向对AI音乐的细粒度结构化评估。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
