CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection
Abstract
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.
Cite
@article{arxiv.2406.02438,
title = {CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection},
author = {Yongyi Zang and Jiatong Shi and You Zhang and Ryuichi Yamamoto and Jionghao Han and Yuxun Tang and Shengyuan Xu and Wenxiao Zhao and Jing Guo and Tomoki Toda and Zhiyao Duan},
journal= {arXiv preprint arXiv:2406.02438},
year = {2024}
}
Comments
Accepted by Interspeech 2024