EnvSDD-Development

This is development set of Environment Sound Deepfake Detection (EnvSDD) dataset. 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 or singing voice. However, environmental sounds have different characteristics, which may make methods for detecting speech and singing deepfakes less effective for real-world sounds. In addition, existing datasets for environmental sound deepfake detection are limited in scale and audio types. To address this gap, we introduce EnvSDD, the first large-scale curated dataset designed for this task, consisting of 45.25 hours of real and 316.74 hours of fake audio. The test set includes diverse conditions to evaluate the generalizability, such as unseen generation models and unseen datasets. We also propose an audio deepfake detection system, based on a pre-trained audio foundation model. Results on EnvSDD show that our proposed system outperforms the state-of-the-art systems from speech and singing domains. Please cite our paper if you want to use the data: Han Yin, et al. "EnvSDD: Benchmarking Environmental Sound Deepfake Detection." Interspeech. 2025.本数据集为环境声音深度伪造检测（Environment Sound Deepfake Detection, EnvSDD）数据集的开发集。 音频生成系统如今可生成极具真实感的声景，既能赋能媒体制作，也潜藏着安全风险。现有研究多聚焦于语音或歌唱音频的深度伪造检测，但环境声音具备独特属性，导致针对语音与歌唱深度伪造的检测方法，在真实环境声音上的检测效果往往欠佳。此外，当前用于环境声音深度伪造检测的数据集，在规模与音频类型方面均存在局限。 为填补这一研究空白，我们构建了EnvSDD——首个专为该任务打造的大规模精选数据集，总计包含45.25小时真实音频与316.74小时伪造音频。测试集设置了多样化的评估条件以检验模型泛化能力，例如涵盖未见过的生成模型与未见过的数据集。此外，我们还基于预训练音频基础模型，提出了一款音频深度伪造检测系统。在EnvSDD上的实验结果表明，我们所提出的系统性能优于语音与歌唱领域的现有顶尖系统。 若您使用本数据集，请引用如下论文： 韩寅（Han Yin）等. 《EnvSDD：环境声音深度伪造检测基准测试》. Interspeech. 2025.