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arXiv · 2504.08365

Location-Oriented Sound Event Localization and Detection with Spatial Mapping and Regression Localization

Xueping Zhang·Yaxiong Chen·Ruilin Yao·Yunfei Zi·Shengwu Xiong
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Abstract

Sound Event Localization and Detection (SELD) combines the Sound Event Detection (SED) with the corresponding Direction Of Arrival (DOA). Recently, adopted event oriented multi-track methods affect the generality in polyphonic environments due to the limitation of the number of tracks. To enhance the generality in polyphonic environments, we propose Spatial Mapping and Regression Localization for SELD (SMRL-SELD). SMRL-SELD segments the 3D spatial space, mapping it to a 2D plane, and a new regression localization loss is proposed to help the results converge toward the location of the corresponding event. SMRL-SELD is location-oriented, allowing the model to learn event features based on orientation. Thus, the method enables the model to process polyphonic sounds regardless of the number of overlapping events. We conducted experiments on STARSS23 and STARSS22 datasets and our proposed SMRL-SELD outperforms the existing SELD methods in overall evaluation and polyphony environments.

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Xueping Zhang, Yaxiong Chen, Ruilin Yao, Yunfei Zi, Shengwu Xiong. 2025-04-11. Location-Oriented Sound Event Localization and Detection with Spatial Mapping and Regression Localization. https://arxiv.org/abs/2504.08365

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