pith. sign in

arxiv: 2405.05244 · v1 · pith:CRERAD7G · submitted 2024-05-08 · eess.AS · cs.AI· cs.MM· cs.SD

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

pith:CRERAD7Gopen to challenge →

classification eess.AS cs.AIcs.MMcs.SD
keywords singingchallengesvddvoicedeepfakedetectionmusicmusical
0
0 comments X
read the original abstract

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).

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Joint Fullband-Subband Modeling for High-Resolution SingFake Detection

    cs.SD 2026-04 unverdicted novelty 7.0

    A joint fullband-subband model using high-resolution 44.1 kHz audio outperforms standard 16 kHz detectors for singing voice deepfake detection by exploiting spectrum-specific synthesis artifacts.