Gate‐Align‐SED: Semi‐Supervised Sound Event Detection via Adaptive Feature Gating and Cross‐Task Alignment in Situation Awareness

Jieli Chen; Li‐Minn Ang; Chee Shen Lim; Kah Phooi Seng; Jeremy Smith

doi:10.1002/aisy.70355

Back

Gate‐Align‐SED: Semi‐Supervised Sound Event Detection via Adaptive Feature Gating and Cross‐Task Alignment in Situation Awareness

Journal article

Open access

Peer reviewed

Gate‐Align‐SED: Semi‐Supervised Sound Event Detection via Adaptive Feature Gating and Cross‐Task Alignment in Situation Awareness

Jieli Chen, Li‐Minn Ang, Chee Shen Lim, Kah Phooi Seng and Jeremy Smith

Advanced Intelligent Systems, Vol.Advanced access

16-Apr-2026

DOI: https://doi.org/10.1002/aisy.70355

Appears in UniSC Supported Open Access Outputs

Files and links (1)

pdf

Advanced Intelligent Systems - 2026 - Chen - Gate‐Align‐SED Semi‐Supervised Sound Event Detection via Adaptive Feature643.96 kBDownload View

Published Version (Advanced Access) Open Access CC BY V4.0

Abstract

disaster monitoring

representation learning

sem-supervised learning

situation awareness

sound event detection

In complex real‐world environments such as disaster monitoring, effective sound event detection (SED) is often hindered by the presence of noise and limited labeled data. This article presents Gate‐Align‐SED, a unified semi‐supervised framework designed to bridge the gap between clip‐level and frame‐level acoustic modeling for disaster‐related audio understanding. The proposed method integrates adaptive feature fusion, mutual attention mechanisms, and a novel label alignment strategy that introduces a learnable correlation matrix to align heterogeneous label granularities. Furthermore, we incorporate a consistency learning paradigm grounded in the Mean‐Teacher framework, promoting robust representation learning across both temporal scales and annotation levels. Experiments demonstrate that the proposed approach enhances both the flexibility and stability of SED systems, particularly under label‐sparse or noisy conditions. Our work offers a scalable and generalizable solution for leveraging both weakly labeled and unlabeled data in critical acoustic event recognition scenarios.

Details

Title: Gate‐Align‐SED: Semi‐Supervised Sound Event Detection via Adaptive Feature Gating and Cross‐Task Alignment in Situation Awareness
Authors: Jieli Chen - Xi’an Jiaotong-Liverpool University
Li‐Minn Ang (Corresponding Author) - University of the Sunshine Coast
Chee Shen Lim - Xi’an Jiaotong-Liverpool University
Kah Phooi Seng - University of the Sunshine Coast
Jeremy Smith - University of Liverpool
Publication details: Advanced Intelligent Systems, Vol.Advanced access
Publisher: Wiley-VCH Verlag GmbH & Co. KGaA
DOI: 10.1002/aisy.70355
ISSN: 2640-4567
Copyright note: © 2026 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Data Availability: The VGGSound dataset can be accessed through a public link: ∼https://www.robots.ox.ac.uk/∼vgg/data/vggsound/.
Organisation Unit: School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 991224995702621
Output Type: Journal article

Metrics

1 Record Views