A Two-Step Learning Framework for Enhancing Sound Event Localization and Detection

Understand

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.