2022/02/17 by Jiangyan Yi, Yi, Jiangyan, Ruibo Fu +36 · 39 citations
Computer Science · Engineering · #Audio analyzer #Audio and Speech Processing (eess.AS) #Audio signal #Audio signal processing #Computer science #Digital Media Forensic Detection #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Multimedia #Music and Audio Processing #Quality (philosophy) #Sound (cs.SD) #Speech and Audio Processing #Speech coding #Speech recognition #Task (project management) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2202.08433
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2022/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.