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SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge Evaluation Plan

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arxiv 2405.05244 v1 pith:CRERAD7G submitted 2024-05-08 eess.AS cs.AIcs.MMcs.SD

You Zhang , Yongyi Zang , Jiatong Shi , Ryuichi Yamamoto , Jionghao Han , Yuxun Tang , Tomoki Toda , Zhiyao Duan This is my paper · ORCID
classification eess.AScs.AIcs.MMcs.SD
keywords singingchallengesvddvoicedeepfakedetectionmusicmusical
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The rapid advancement of AI-generated singing voices, which now closely mimic natural human singing and align seamlessly with musical scores, has led to heightened concerns for artists and the music industry. Unlike spoken voice, singing voice presents unique challenges due to its musical nature and the presence of strong background music, making singing voice deepfake detection (SVDD) a specialized field requiring focused attention. To promote SVDD research, we recently proposed the "SVDD Challenge," the very first research challenge focusing on SVDD for lab-controlled and in-the-wild bonafide and deepfake singing voice recordings. The challenge will be held in conjunction with the 2024 IEEE Spoken Language Technology Workshop (SLT 2024).

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  1. Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake Detection

    eess.AS 2025-02 conditional novelty 5.0 of 10

    Across six self-supervised speech models and ten deepfake datasets, the first 4-12 transformer layers match full-model fake audio detection performance, reducing parameters by at least half.

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