1 citations · 1 across the 3 of their papers we have counts for
10 papers
MADBench: A Benchmark for Modality-Aware Audio Deepfake Detection
Yanqiu Li, Yang Xiao, Jisheng Bai +3
Recent advances in speech synthesis and audio generation have made high-fidelity acoustic forgery low-cost and difficult to attribute, enabling a realistic attack scenario in which…
Audio-Language Models for Audio-Centric Tasks: A Systematic Survey
Yi Su, Jisheng Bai, Qisheng Xu +2
Audio-Language Models (ALMs), trained on paired audio-text data, are designed to process, understand, and reason about audio-centric multimodal content. Unlike traditional supervis…
The First Environmental Sound Deepfake Detection Challenge: Benchmarking Robustness, Evaluation, and Insights
Han Yin, Yang Xiao, Rohan Kumar Das +2
Recent progress in audio generation has made it increasingly easy to create highly realistic environmental soundscapes, which can be misused to produce deceptive content, such as f…
Environmental Sound Deepfake Detection Challenge: An Overview
Han Yin, Yang Xiao, Rohan Kumar Das +2
Recent progress in audio generation models has made it possible to create highly realistic and immersive soundscapes, which are now widely used in film and virtual-reality-related…
ESDD 2026: Environmental Sound Deepfake Detection Challenge Evaluation Plan
Han Yin, Yang Xiao, Rohan Kumar Das +2
Recent advances in audio generation systems have enabled the creation of highly realistic and immersive soundscapes, which are increasingly used in film and virtual reality. Howeve…
EnvSDD: Benchmarking Environmental Sound Deepfake Detection
Han Yin, Yang Xiao, Rohan Kumar Das +4
Audio generation systems now create very realistic soundscapes that can enhance media production, but also pose potential risks. Several studies have examined deepfakes in speech o…