  Recent singing voice synthesis and conversion advancements necessitate robust
singing voice deepfake detection (SVDD) models. Current SVDD datasets face
challenges due to limited controllability, diversity in deepfake methods, and
licensing restrictions. Addressing these gaps, we introduce CtrSVDD, a
large-scale, diverse collection of bonafide and deepfake singing vocals. These
vocals are synthesized using state-of-the-art methods from publicly accessible
singing voice datasets. CtrSVDD includes 47.64 hours of bonafide and 260.34
hours of deepfake singing vocals, spanning 14 deepfake methods and involving
164 singer identities. We also present a baseline system with flexible
front-end features, evaluated against a structured train/dev/eval split. The
experiments show the importance of feature selection and highlight a need for
generalization towards deepfake methods that deviate further from training
distribution. The CtrSVDD dataset and baselines are publicly accessible.
