SemiPL: A Semi-supervised Method for Event Sound Source Localization

TL;DR

SemiPL introduces a semi-supervised approach to enhance event sound source localization in complex, chaotic environments, improving performance over existing models by leveraging parameter tuning and semi-supervised learning techniques.

Contribution

The paper proposes SemiPL, a semi-supervised method that improves sound source localization accuracy in complex datasets, extending previous contrastive learning frameworks.

Findings

SemiPL achieves 12.2% improvement in cIoU on Chaotic World dataset.

Parameter tuning positively impacts model performance.

SemiPL outperforms existing models in complex event scenarios.

Abstract

In recent years, Event Sound Source Localization has been widely applied in various fields. Recent works typically relying on the contrastive learning framework show impressive performance. However, all work is based on large relatively simple datasets. It's also crucial to understand and analyze human behaviors (actions and interactions of people), voices, and sounds in chaotic events in many applications, e.g., crowd management, and emergency response services. In this paper, we apply the existing model to a more complex dataset, explore the influence of parameters on the model, and propose a semi-supervised improvement method SemiPL. With the increase in data quantity and the influence of label quality, self-supervised learning will be an unstoppable trend. The experiment shows that the parameter adjustment will positively affect the existing model. In particular, SSPL achieved an improvement of 12.2% cIoU and 0.56% AUC in Chaotic World compared to the results provided. The code is available at: https://github.com/ly245422/SSPL