From General-Purpose Audio Tagging to Spatially Grounded Sound Event Localization and Detection
arXiv:2606.27751v1 Announce Type: cross Abstract: This report investigates the extension of pretrained General-Purpose Audio Tagging (GP-AT) models toward spatially grounded Sound Event Localization and Detection (SELD). The proposed AT2SELD framework couples a pretrained AT backbone with compact First-Order Ambisonics (FOA) spatial processing, track-wise SED and Cartesian DOA estimation, permutation aware supervision, and calibration. It characterizes how semantic audio priors support localization-aware scene analysis under data, computation, and deployment constraints. The framework is devel
The rapid advancement in general audio processing through large models is creating opportunities to integrate specialized spatial understanding, pushing practical applications of sound recognition.
This research enables more sophisticated environmental understanding for AI systems by integrating spatial data with sound event detection, moving beyond simple classification to contextual awareness.
AI models can now interpret not just what a sound is, but also where it originates from, enhancing capabilities for autonomous systems and intelligent environments.
- · Autonomous vehicle developers
- · Robotics companies
- · Smart home technology
- · Security systems providers
Improved situational awareness for AI-powered devices in complex environments.
Reduced need for extensive labeled spatial audio datasets as pre-trained models are adapted.
New forms of human-computer interaction based on 3D sound detection and localization.
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Read at arXiv cs.AI