Stereo sound event localization and detection based on PSELDnet pretraining and BiMamba sequence modeling

TL;DR

This paper introduces a stereo sound event localization and detection system that leverages pre-trained PSELDnet and a novel BiMamba sequence model, achieving improved accuracy and reduced complexity.

Contribution

The work replaces the Conformer with a BiMamba module and uses asymmetric convolutions, enhancing spatiotemporal modeling in SELD tasks.

Findings

Outperforms baseline and original PSELDnet models

Achieves better accuracy on DCASE2025 Task 3 dataset

Reduces computational complexity

Abstract

Pre-training methods have achieved significant performance improvements in sound event localization and detection (SELD) tasks, but existing Transformer-based models suffer from high computational complexity. In this work, we propose a stereo sound event localization and detection system based on pre-trained PSELDnet and bidirectional Mamba sequence modeling. We replace the Conformer module with a BiMamba module and introduce asymmetric convolutions to more effectively model the spatiotemporal relationships between time and frequency dimensions. Experimental results demonstrate that the proposed method achieves significantly better performance than the baseline and the original PSELDnet with Conformer decoder architecture on the DCASE2025 Task 3 development dataset, while also reducing computational complexity. These findings highlight the effectiveness of the BiMamba architecture in addressing the challenges of the SELD task.