构建合成音乐检测数据集FakeMusicCaps，助力音频溯源与版权保护。
FakeMusicCaps: a Dataset for Detection and Attribution of Synthetic Music Generated via Text-to-Music Models
- 基于MusicCaps数据集，用多款顶尖文生音乐模型生成伪音乐数据。
- 首次在闭集与开集场景下验证合成音乐的可检测性与归属识别能力。
- 适合音频取证、版权追踪及生成内容监管领域的研究者使用。
文生音乐（TTM）模型近期彻底改变了自动音乐生成领域，其性能超越所有先前最优模型，且显著降低使用门槛。由于广泛应用，此类技术已进入商业音乐制作，引发版权侵犯与归属认定等严峻问题，亟需音频取证领域关注。本文提出数据集FakeMusicCaps，包含由多个前沿TTM技术重新生成的MusicCaps音乐-标题配对数据。通过初步实验，评估该数据集在闭集与开集分类场景下对合成音乐的检测与归属能力，为后续研究提供基准支持。
原文摘要 · Abstract (English)
Text-To-Music (TTM) models have recently revolutionized the automatic music generation research field. Specifically, by reaching superior performances to all previous state-of-the-art models and by lowering the technical proficiency needed to use them. Due to these reasons, they have readily started to be adopted for commercial uses and music production practices. This widespread diffusion of TTMs poses several concerns regarding copyright violation and rightful attribution, posing the need of serious consideration of them by the audio forensics community. In this paper, we tackle the problem of detection and attribution of TTM-generated data. We propose a dataset, FakeMusicCaps that contains several versions of the music-caption pairs dataset MusicCaps re-generated via several state-of-the-art TTM techniques. We evaluate the proposed dataset by performing initial experiments regarding the detection and attribution of TTM-generated audio.
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