The rise of singing voice synthesis presents critical challenges to artists
and industry stakeholders over unauthorized voice usage. Unlike synthesized
speech, synthesized singing voices are typically released in songs containing
strong background music that may hide synthesis artifacts. Additionally,
singing voices present different acoustic and linguistic characteristics from
speech utterances. These unique properties make singing voice deepfake
detection a relevant but significantly different problem from synthetic speech
detection. In this work, we propose the singing voice deepfake detection task.
We first present SingFake, the first curated in-the-wild dataset consisting of
28.93 hours of bonafide and 29.40 hours of deepfake song clips in five
languages from 40 singers. We provide a train/val/test split where the test
sets include various scenarios. We then use SingFake to evaluate four
state-of-the-art speech countermeasure systems trained on speech utterances. We
find these systems lag significantly behind their performance on speech test
data. When trained on SingFake, either using separated vocal tracks or song
mixtures, these systems show substantial improvement. However, our evaluations
also identify challenges associated with unseen singers, communication codecs,
languages, and musical contexts, calling for dedicated research into singing
voice deepfake detection. The SingFake dataset and related resources are
available online.

提出了歌声深度伪造检测任务，创建了第一个真实数据集 SingFake，包含 40 位歌手 5 种语言的 28.93 小时真实歌曲和 29.40 小时深度伪造歌曲片段，评估了四个最先进的口语对抗系统，发现在 SingFake 上训练相对于在口语测试数据上训练有显著改进，但也提出了未知歌手、通信编解码器、语言和音乐背景等挑战。