BEAT2AASIST: BEATs Feature Splitting with Dual-Branch AASIST for Environmental Sound Deepfake Detection

摘要

Recent advances in text-to-audio (TTA) and audio-to-audio (ATA) generation models have enabled the creation of highly realistic environmental sounds, raising growing concerns about malicious audio manipulation in real-world scenarios. To address this emerging threat, the ESDD 2026 Challenge was introduced as the first large-scale benchmark for Environmental Sound Deepfake Detection (ESDD), featuring two tracks that evaluate generalization to unseen generators and robustness under black-box, low-resource conditions. In this paper, we present BEAT2AASIST, an enhanced deepfake detection framework built upon the BEATs-AASIST baseline. Motivated by the observation that token-based audio representations may weaken explicit preservation of structured acoustic cues, the proposed method introduces a dual-branch AASIST architecture that explicitly splits BEATs-derived representations along frequency or channel dimensions. This design enables specialized modeling of complementary spoofing artifacts that may be attenuated in unified representations. To further enrich acoustic features, we incorporate multi-layer fusion strategies that aggregate information from multiple transformer layers using concatenation, CNN-gated, and SE-gated mechanisms. In addition, vocoder-based data augmentation with multiple high-fidelity neural vocoders is employed to enhance robustness against unseen and black-box spoofing attacks. Experimental results on the EnvSDD dataset demonstrate that BEAT2AASIST achieves strong and consistent performance across both challenge tracks. In particular, the proposed approach attains 3rd place in Track 2 and 4th place in Track 1 in the ESDD 2026 Challenge, despite using fewer ensemble components than top-ranked systems. These results suggest that explicit modeling of heterogeneous acoustic subspaces, combined with targeted representation fusion and data augmentation, provides an effective and efficient design strategy for real-world environmental sound deepfake detection. The code is available at https://github.com/ikwak2/BEAT2AASIST.