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MSFDnet: A Multi-Scale Feature Dual-Layer Fusion Model for Sound Event Localization and Detection
Yi Chen1, Zhenyu Huang2, Liang Lei3
1School of Big Data and Information Industry, Chongqing City Management College, No. 151, Daxuecheng South Second Road, Shapingba District, Chongqing 401331, China.
Sensors (Basel, Switzerland)
|October 29, 2025
Summary
This study introduces MSDFnet, a novel model for Sound Event Localization and Detection (SELD) that improves accuracy in complex audio by enhancing feature extraction and fusion. The new model excels in dynamic scenarios, outperforming existing methods.
Area of Science:
- Acoustics
- Machine Learning
- Signal Processing
Background:
- Existing Sound Event Localization and Detection (SELD) methods struggle with long-duration, dynamic audio due to insufficient feature extraction and underutilization of multi-task feature complementarity.
- This limitation restricts system performance in complex acoustic environments.
Purpose of the Study:
- To propose a novel SELD model, MSDFnet, designed to overcome the limitations of current methods in dynamic audio scenarios.
- To enhance the accuracy of sound event detection and localization by improving feature extraction and fusion strategies.
Main Methods:
- Developed MSDFnet, incorporating a Multi-Scale Feature Aggregation (MSFA) module for capturing diverse spatial features.
- Implemented a Dual-Layer Feature Fusion (DLFF) strategy to strengthen the relationship between Sound Event Detection (SED) and Direction of Arrival (DOA) features.
- Evaluated the model on the DCASE2020 Task 3 dataset.
Main Results:
- MSDFnet achieved competitive scores on the DCASE2020 Task 3 dataset, including ER20 of 0.319, F20 of 76%, LEcd of 10.2°, LRcd of 82.4%, and SELDscore of 0.198.
- The model demonstrated excellent performance in complex audio scenarios.
- Ablation studies confirmed the significant contributions of the MSFA and DLFF modules to the overall performance.
Conclusions:
- MSDFnet effectively addresses limitations in existing SELD methods for dynamic audio.
- The proposed MSFA and DLFF modules significantly enhance sound event localization and detection accuracy.
- The model shows strong potential for real-world applications in complex acoustic environments.
