  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/validation/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 at https://www.singfake.org/.
