Work

MSFDnet: A Multi-Scale Feature Dual-Layer Fusion Model for Sound Event Localization and Detection

Year: 2025

Type: article

Abstract: The task of Sound Event Localization and Detection (SELD) aims to simultaneously address sound event recognition and spatial localization. However, existing SELD methods face limitations in long-duration dynamic audio scenarios, as they do not fully leverage the complementarity between multi-task features and lack depth in feature extraction, leading to restricted system performance. To address these issues, we propose a novel SELD model-MSDFnet. By introducing a Multi-Scale Feature Aggregation (MSFA) module and a Dual-Layer Feature Fusion strategy (DLFF), MSDFnet captures rich spatial features at multiple scales and establishes a stronger complementary relationship between SED and DOA features, thereby enhancing detection and localization accuracy. On the DCASE2020 Task 3 dataset, our model achieved scores of 0.319, 76%, 10.2°, 82.4%, and 0.198 in ER20,F20, LEcd, LRcd, and SELDscore metrics, respectively. Experimental results demonstrate that MSDFnet performs excellently in complex audio scenarios. Additionally, ablation studies further confirm the effectiveness of the MSFA and DLFF modules in enhancing SELD task performance.

Source: Sensors

DOI: 10.3390/s25206479

Authors: Yi Chen, Zhenyu Huang, Liang Lei, Yu Yuan

Institutions: Chongqing City Management University, Chongqing Vocational College of Economics and Trade, Chongqing University of Science and Technology

Language: English

Topic: Music and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Chongqing Municipal Education Commission