8 papers
AT-ADD: A Benchmark and Challenge for Robust and All-Type Audio Deepfake Detection
Yuankun Xie, Haonan Cheng, Jiayi Zhou +11
Recent audio generation models can synthesize high-fidelity speech, environmental sound, singing voice, and music, creating new risks for multimedia trust. Existing audio deepfake…
AT-ADD: All-Type Audio Deepfake Detection Challenge Summary
Yuankun Xie, Haonan Cheng, Jiayi Zhou +11
This paper summarizes the ACM Multimedia 2026 AT-ADD Grand Challenge on all-type audio deepfake detection. AT-ADD contains two tracks: robust speech deepfake detection under realis…
UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions
Chunyu Qiang, Xiaopeng Wang, Kang Yin +11
Generative audio modeling has largely been fragmented into specialized tasks, text-to-speech (TTS), text-to-music (TTM), and text-to-audio (TTA), each operating under heterogeneous…
AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan
Yuankun Xie, Haonan Cheng, Jiayi Zhou +10
The rapid advancement of Audio Large Language Models (ALLMs) has enabled cost-effective, high-fidelity generation and manipulation of both speech and non-speech audio, including so…
DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer
Yisu Liu, Chenxing Li, Wanqian Zhang +6
Controllable text-to-audio generation aims to synthesize audio from textual descriptions while satisfying user-specified constraints, including event types, temporal sequences, and…
When Audio Generators Become Good Listeners: Generative Features for Understanding Tasks
Zeyu Xie, Chenxing Li, Xuenan Xu +6
This work pioneers the utilization of generative features in enhancing audio understanding. Unlike conventional discriminative features that directly optimize posterior and thus em…