ESDD 2026: Environmental Sound Deepfake Detection Challenge Evaluation Plan

TL;DR

This paper introduces EnvSDD, a large-scale dataset for environmental sound deepfake detection, and outlines the evaluation plan for the ESDD 2026 challenge to improve detection methods in real-world scenarios.

Contribution

It presents the first large-scale curated dataset for environmental sound deepfake detection and details the evaluation plan for the upcoming challenge at ICASSP 2026.

Findings

Introduction of EnvSDD dataset with 45.25 hours real and 316.7 hours fake sounds

Design of two challenge tracks: Unseen Generators and Black-Box Low-Resource

Preparation for a large-scale evaluation at ICASSP 2026

Abstract

Recent advances in audio generation systems have enabled the creation of highly realistic and immersive soundscapes, which are increasingly used in film and virtual reality. However, these audio generators also raise concerns about potential misuse, such as generating deceptive audio content for fake videos and spreading misleading information. Existing datasets for environmental sound deepfake detection (ESDD) are limited in scale and audio types. To address this gap, we have proposed EnvSDD, the first large-scale curated dataset designed for ESDD, consisting of 45.25 hours of real and 316.7 hours of fake sound. Based on EnvSDD, we are launching the Environmental Sound Deepfake Detection Challenge. Specifically, we present two different tracks: ESDD in Unseen Generators and Black-Box Low-Resource ESDD, covering various challenges encountered in real-life scenarios. The challenge will be held in conjunction with the 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2026).