BUT Systems for Environmental Sound Deepfake Detection in the ESDD 2026 Challenge
Abstract
This paper describes the BUT submission to the ESDD 2026 Challenge, specifically focusing on Track 1: Environmental Sound Deepfake Detection with Unseen Generators. To address the critical challenge of generalizing to audio generated by unseen synthesis algorithms, we propose a robust ensemble framework leveraging diverse Self-Supervised Learning (SSL) models. We conduct a comprehensive analysis of general audio SSL models (including BEATs, EAT, and Dasheng) and speech-specific SSLs. These front-ends are coupled with a lightweight Multi-Head Factorized Attention (MHFA) back-end to capture discriminative representations. Furthermore, we introduce a feature domain augmentation strategy based on distribution uncertainty modeling to enhance model robustness against unseen spectral distortions. All models are trained exclusively on the official EnvSDD data, without using any external resources. Experimental results demonstrate the effectiveness of our approach: our best single system achieved Equal Error Rates (EER) of 0.00\%, 4.60\%, and 4.80\% on the Development, Progress (Track 1), and Final Evaluation sets, respectively. The fusion system further improved generalization, yielding EERs of 0.00\%, 3.52\%, and 4.38\% across the same partitions.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper makes a significant contribution to the field of audio deepfake detection by introducing an innovative ensemble framework that effectively utilizes self-supervised learning models and advanced attention mechanisms. The methodology is well-founded, and the experimental results indicate a strong potential for real-world applications in audio security and verification.
Methodology
The paper presents a robust ensemble framework for Environmental Sound Deepfake Detection, leveraging diverse Self-Supervised Learning (SSL) models and a Multi-Head Factorized Attention (MHFA) mechanism. The integration of feature domain augmentation based on distribution uncertainty modeling is particularly innovative, enhancing the model's robustness against unseen spectral distortions. The systematic comparison between general audio and speech-specific SSL models is well-structured, providing valuable insights into their respective performances in the context of deepfake detection.
Results & evaluation
The experimental setup is rigorous, utilizing the official EnvSDD dataset exclusively, which ensures that the results are directly relevant to the challenge. The reported Equal Error Rates (EER) demonstrate significant improvements over baseline models, particularly with the ensemble system achieving an EER of 3.52%. The results are well-documented, and the analysis of the impact of different SSL models and augmentation strategies is thorough.
Reproducibility
The paper provides sufficient implementation details, including the training configuration, optimizer settings, and model architecture. The use of an open-source framework (WeDefense) for implementation enhances the reproducibility of the results. However, the lack of access to the dataset used for training may limit full reproducibility for external researchers.
Limitations
While the approach shows promising results, the reliance on a single dataset may limit the generalizability of the findings. Additionally, the performance metrics primarily focus on EER, which may not capture all aspects of model performance, such as precision and recall in real-world applications.
Broader impact
The proposed methods have significant implications for the field of audio deepfake detection, particularly in applications related to security and content verification. By addressing the challenges of generalization to unseen generators, this work contributes to the ongoing efforts to develop robust anti-spoofing technologies in audio processing. This paper makes a significant contribution to the field of audio deepfake detection by introducing an innovative ensemble framework that effectively utilizes self-supervised learning models and advanced attention mechanisms. The methodology is well-founded, and the experimental results indicate a strong potential for real-world applications in audio security and verification.
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