RealDESED: A Real-World Domestic Sound Event Detection Benchmark

2026-07-18

AI Summary

This work addresses the limitations of existing sound event detection datasets, which often rely on synthetic or web-sourced audio and lack the diversity and annotation reliability of real-world home environments. To bridge this gap, the authors introduce a new benchmark dataset comprising 5,710 naturally recorded household audio clips spanning 15 common sound event classes. For the first time in this domain, a multi-annotator scheme coupled with a rigorous validation protocol is employed to ensure label quality, and rich metadata—including recording device, location, and environmental context—is provided. A Transformer-based baseline model, enhanced with strategies for annotation aggregation, post-processing, long-audio inference, and metadata fusion, achieves a macro-averaged PSDS1 score of 0.731 on the test set, establishing a high-quality benchmark and a robust evaluation framework for sound event detection in realistic domestic settings.

Abstract

This paper presents RealDESED, a real-world domestic sound event detection (SED) benchmark comprising 5,710 audio recordings collected by 652 participants in their homes. Each recording is between 15 and 35 seconds long and contains temporally precise annotations for 15 common domestic sound classes. In contrast to existing SED datasets, which typically rely on simulated soundscapes or broad web-crawled audio, RealDESED consists exclusively of recordings captured in natural domestic environments, reflecting realistic variability in recording devices, device placement, acoustic conditions, background sounds, and naturally occurring event co-occurrences. A distinguishing characteristic of the dataset is its multi-annotator labeling scheme, where each recording is independently annotated by multiple annotators, while the validation and test sets undergo an additional review process to ensure high annotation quality and reliable benchmarking. Furthermore, the dataset provides rich metadata, including recording device, device placement, environment labels, and textual scene descriptions. We establish a strong transformer-based baseline and investigate annotation aggregation strategies, post-processing methods, long-form inference, and the impact of recording metadata on model performance. Our baseline achieves a macro-averaged PSDS1 score of 0.731 on the test set. We believe RealDESED provides a valuable benchmark for developing and evaluating robust SED systems under realistic domestic conditions, helping to bridge the gap between current research benchmarks and real-world deployment.

Problem

• sound event detection
• real-world audio
• domestic environment
• dataset benchmark
• annotation variability

Innovation

• RealDESED
• sound event detection
• real-world audio
• multi-annotator labeling
• domestic environment