Year: 2025
Type: preprint
Abstract: Sound Event Localization and Detection (SELD) is crucial in spatial audio processing, enabling systems to detect sound events and estimate their 3D directions. Existing SELD methods use single- or dual-branch architectures: single-branch models share SED and DoA representations, causing optimization conflicts, while dual-branch models separate tasks but limit information exchange. To address this, we propose a two-step learning framework. First, we introduce a tracwise reordering format to maintain temporal consistency, preventing event reassignments across tracks. Next, we train SED and DoA networks to prevent interference and ensure task-specific feature learning. Finally, we effectively fuse DoA and SED features to enhance SELD performance with better spatial and event representation. Experiments on the 2023 DCASE challenge Task 3 dataset validate our framework, showing its ability to overcome single- and dual-branch limitations and improve event classification and localization.
Source: arXiv (Cornell University)
Author: Yu, Hogeon
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6948415064
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