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arXiv · 2512.22156

A Robust framework for sound event localization and detection on real recordings

Jin Sob Kim·Hyun Joon Park·Wooseok Shin·Sung Won Han
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Abstract

This technical report describes the systems submitted to the DCASE2022 challenge task 3: sound event localization and detection (SELD). The task aims to detect occurrences of sound events and specify their class, furthermore estimate their position. Our system utilizes a ResNet-based model under a proposed robust framework for SELD. To guarantee the generalized performance on the real-world sound scenes, we design the total framework with augmentation techniques, a pipeline of mixing datasets from real-world sound scenes and emulations, and test time augmentation. Augmentation techniques and exploitation of external sound sources enable training diverse samples and keeping the opportunity to train the real-world context enough by maintaining the number of the real recording samples in the batch. In addition, we design a test time augmentation and a clustering-based model ensemble method to aggregate confident predictions. Experimental results show that the model under a proposed framework outperforms the baseline methods and achieves competitive performance in real-world sound recordings.

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Jin Sob Kim, Hyun Joon Park, Wooseok Shin, Sung Won Han. 2025-12-16. A Robust framework for sound event localization and detection on real recordings. https://arxiv.org/abs/2512.22156

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