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Published VersionCC BY V4.0, Open Access
Journal article
Towards a Gated Graph Neural Network with an Attention Mechanism for Audio Features with a Situation Awareness Application
Electronics, Vol.14(13), pp.1-30
2025
Abstract
Situation awareness (SA) involves analyzing sensory data, such as audio signals, to identify anomalies. While acoustic features are widely used in audio analysis, existing methods face critical limitations; they often overlook the relevance of SA audio segments, failing to capture the complex relational patterns in audio data that are essential for SA. In this study, we first propose a graph neural network (GNN) with an attention mechanism that models SA audio features through graph structures, capturing both node attributes and their relationships for richer representations than traditional methods. Our analysis identifies suitable audio feature combinations and graph constructions for SA tasks. Building on this, we introduce a situation awareness gated-attention GNN (SAGA-GNN), which dynamically filters irrelevant nodes through max-relevance neighbor sampling to reduce redundant connections, and a learnable edge gated-attention mechanism that suppresses noise while amplifying critical events. The proposed method employs sigmoid-activated attention weights conditioned on both node features and temporal relationships, enabling adaptive node emphasizing for different acoustic environments. Experiments reveal that the proposed graph-based audio features demonstrate superior representation capacity compared to traditional methods. Additionally, both proposed graph-based methods outperform existing approaches. Specifically, owing to the combination of graph-based audio features and dynamic selection of audio nodes based on gated-attention, SAGA-GNN achieved superior results on two real datasets. This work underscores the importance and potential value of graph-based audio features and attention mechanism-based GNNs, particularly in situational awareness applications.
Details
- Title
- Towards a Gated Graph Neural Network with an Attention Mechanism for Audio Features with a Situation Awareness Application
- Authors
- Jieli Chen - Xi’an Jiaotong-Liverpool UniversityKah Phooi Seng (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringLi Minn Ang - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringJeremy Smith - University of LiverpoolHanyue Xu - Xi’an Jiaotong-Liverpool University
- Publication details
- Electronics, Vol.14(13), pp.1-30
- Publisher
- MDPI AG
- Date published
- 2025
- DOI
- 10.3390/electronics14132621
- ISSN
- 2079-9292
- Copyright note
- © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Data Availability
- The data used in this study is publicly available in [Koizumi, Y.; Kawaguchi, Y.; Imoto, K.; Nakamura, T.; Nikaido, Y.; Tanabe, R.; Purohit, H.; Suefusa, K.; Endo, T.; Yasuda, M.; et al. Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring. arXiv 2020].
- Organisation Unit
- School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 991153837002621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Physics, Applied