| Title |
Graph-Enhanced Dual-Stream Feature Fusion With Pre-Trained Model For Acoustic Traffic Monitoring |
| ID_Doc |
28293 |
| Authors |
Fan S.; Xiao F.; Wang W.; Qi S.; Zhu Q.; Wang W.; Guan J. |
| Year |
2025 |
| Published |
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| DOI |
http://dx.doi.org/10.1109/ICASSP49660.2025.10889208 |
| Abstract |
Microphone array techniques are widely used in sound source localization and smart city acoustic-based traffic monitoring, but these applications face significant challenges due to the scarcity of labeled real-world traffic audio data and the complexity and diversity of application scenarios. The DCASE Challenge's Task 10 focuses on using multi-channel audio signals to count vehicles (cars or commercial vehicles) and identify their directions (left-to-right or vice versa). In this paper, we propose a graph-enhanced dual-stream feature fusion network (GEDF-Net) for acoustic traffic monitoring, which simultaneously considers vehicle type and direction to improve detection. We propose a graph-enhanced dual-stream feature fusion strategy which consists of a vehicle type feature extraction (VTFE) branch, a vehicle direction feature extraction (VDFE) branch, and a frame-level feature fusion module to combine the type and direction feature for enhanced performance. A pre-trained model (PANNs) is used in the VTFE branch to mitigate data scarcity and enhance the type features, followed by a graph attention mechanism to exploit temporal relationships and highlight important audio events within these features. The frame-level fusion of direction and type features enables fine-grained feature representation, resulting in better detection performance. Experiments demonstrate the effectiveness of our proposed method. GEDF-Net is our submission that achieved 1st place in the DCASE 2024 Challenge Task 10. © 2025 IEEE. |
| Author Keywords |
Acoustic-based traffic monitoring; feature fusion; graph attention; pre-trained model; transfer learning |
