# SVDD 2024: The Inaugural Singing Voice Deepfake Detection Challenge Source: [SVDD 2024: The Inaugural Singing Voice Deepfake Detection Challenge]() Collection index: [llms.txt]() Data updated: 2026-01-28T05:00:19.604Z Authors: You Zhang, Yongyi Zang, Jiatong Shi, Ryuichi Yamamoto, Tomoki Toda, Zhiyao Duan Published: 2024-08-28T20:48:04.000Z ## Abstract With the advancements in singing voice generation and the growing presence of AI singers on media platforms, the inaugural Singing Voice Deepfake Detection (SVDD) Challenge aims to advance research in identifying AI-generated singing voices from authentic singers. This challenge features two tracks: a controlled setting track (CtrSVDD) and an in-the-wild scenario track (WildSVDD). The CtrSVDD track utilizes publicly available singing vocal data to generate deepfakes using state-of-the-art singing voice synthesis and conversion systems. Meanwhile, the WildSVDD track expands upon the existing SingFake dataset, which includes data sourced from popular user-generated content websites. For the CtrSVDD track, we received submissions from 47 teams, with 37 surpassing our baselines and the top team achieving a 1.65% equal error rate. For the WildSVDD track, we benchmarked the baselines. This paper reviews these results, discusses key findings, and outlines future directions for SVDD research. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Singing Voice Deepfake Detection on CtrSVDD](): Singing Voice Deepfake Detection; CtrSVDD; EER: 13.8. ## Original sources and implementation [Original paper]() [PDF]()