FakeMusicCaps: a Dataset for Detection and Attribution of Synthetic Music Generated via Text-to-Music Models

Luca Comanducci, Paolo Bestagini, Stefano Tubaro
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Abstract

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.
FakeMusicCaps:通过文本到音乐模型生成的合成音乐的检测和归属数据集
文本到音乐(TTM)模型最近在自动音乐生成研究领域掀起了一场革命。具体来说,TTM 模型的性能优于以往所有最先进的模型,而且降低了使用这些模型所需的技术熟练度。由于这些原因,它们已开始被商业用途和音乐制作实践所采用。TTM 的广泛应用带来了一些有关侵犯版权和合法归属的问题,需要音频取证界认真考虑。在本文中,我们探讨了 TTM 生成数据的检测和归属问题。我们提出了一个名为 "FakeMusicCaps "的数据集,其中包含通过几种最先进的 TTM 技术生成的多个版本的音乐字幕对数据集 MusicCaps。我们通过对 TTM 生成的音频进行检测和归属的初步实验,对所提出的数据集进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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