EnvSDD: Benchmarking Environmental Sound Deepfake Detection

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.

ASiT: Audio Spectrogram vIsion Transformer for General Audio Representation

Vision transformers, which were originally developed for natural language processing, have recently generated significant interest in the computer vision and audio communities due to their flexibility in learning long-range relationships. Constrained by data hungry nature of transformers and limited labelled data most transformer-based models for audio tasks are finetuned from ImageNet pretrained models, despite the huge gap between the natural images domain and audio domain. This has motivated the research in self-supervised pretraining of audio transformers, which reduces the dependency on large amounts of labeled data and focuses on extracting concise representation of the audio spectrograms. In this paper, we propose ASiT, a novel self-supervised transformer for general audio representations that captures local and global contextual information employing group masked model learning and self-distillation. We evaluate our pretrained models on both audio and speech classification tasks including audio event classification, keyword spotting, and speaker identification. We further conduct comprehensive ablation studies, including evaluations of different pretraining strategies. The proposed ASiT framework significantly boosts the performance on all tasks and sets a new state-of-the-art performance on five audio and speech classification tasks, outperforming recent methods, including the approaches that use additional datasets for pretraining. The code and pretrained weights will be made publicly available for the scientific community.