# EnvSDD: Benchmarking Environmental Sound Deepfake Detection

arXiv: https://arxiv.org/abs/2505.19203 · HTML: https://mlsanity.com/paper/2505.19203/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 25 May 2025.

## Builds on (most cited first, 22 of 22)

- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [Clotho: An Audio Captioning Dataset](https://mlsanity.com/paper/1910.09387/) (2019)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [Masked Autoencoders that Listen](https://mlsanity.com/paper/2207.06405/) (2022)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection](https://mlsanity.com/paper/2107.12710/) (2021)
- [TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization](https://mlsanity.com/paper/2412.21037/) (2024)
- [Foley Sound Synthesis at the DCASE 2023 Challenge](https://mlsanity.com/paper/2304.12521/) (2023)
- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [SVDD 2024: The Inaugural Singing Voice Deepfake Detection Challenge](https://mlsanity.com/paper/2408.16132/) (2024)
- [SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection](https://mlsanity.com/paper/2211.06073/) (2022)
- [FakeSound: Deepfake General Audio Detection](https://mlsanity.com/paper/2406.08052/) (2024)
- [Detection of Deepfake Environmental Audio](https://mlsanity.com/paper/2403.17529/) (2024)

## Built on by (most cited first, 11 of 11)

- [Interpretable All-Type Audio Deepfake Detection with Audio LLMs via Frequency-Time Reinforcement Learning](https://mlsanity.com/paper/2601.02983/) (2026)
- [Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTOR](https://mlsanity.com/paper/2603.06164/) (2026)
- [AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan](https://mlsanity.com/paper/2604.08184/) (2026)
- [AUDDT: A Unified Benchmark Toolkit for Audio and Speech Deepfake Detectors](https://mlsanity.com/paper/2509.21597/) (2025)
- [AudioMosaic: Contrastive Masked Audio Representation Learning](https://mlsanity.com/paper/2605.14231/) (2026)
- [BEAT2AASIST model with layer fusion for ESDD 2026 Challenge](https://mlsanity.com/paper/2512.15180/) (2025)
- [MoEScore: Mixture-of-Experts-Based Text-Audio Relevance Score Prediction for Text-to-Audio System Evaluation](https://mlsanity.com/paper/2601.06829/) (2026)
- [Environmental Sound Deepfake Detection Using Deep-Learning Framework](https://mlsanity.com/paper/2604.19652/) (2026)
- [SynSFX: Multi-Model Sound Effects Synthesis Dataset for Deepfake Detection and Evaluation](https://mlsanity.com/paper/2607.04848/) (2026)
- [MADBench: A Benchmark for Modality-Aware Audio Deepfake Detection](https://mlsanity.com/paper/2608.09593/) (2026)
- [ToolDF: Tool-Integrated Reasoning for Mixed-Authenticity Audio Deepfake Detection](https://mlsanity.com/paper/2609.03620/) (2026)

## Abstract (arXiv)

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.