用BiMamba替代Transformer，提升立体声事件检测效率与精度
Enhancing Stereo Sound Event Detection with BiMamba and Pretrained PSELDnet
- 用双向Mamba替换Conformer模块，降低计算开销
- 在DCASE2025数据集上优于基线和原PSELDnet模型
- 适合关注高效音频事件定位的工程师与研究者
预训练方法显著提升了声音事件定位与检测（SELD）性能。然而，现有基于Transformer的模型仍存在计算成本高的问题。为此，本文提出一种使用预训练PSELDnet和双向Mamba序列模型的立体声SELD系统。具体地，将Conformer模块替换为BiMamba模块，并采用非对称卷积更好地捕捉音频信号中的时频关系。在DCASE2025任务3开发数据集上的测试结果显示，该方法性能优于基线模型和原始的PSELDnet（带Conformer解码器），同时所需计算资源更少。结果表明，BiMamba架构能有效应对SELD任务中的关键挑战。源代码已公开于https://github.com/alexandergwm/DCASE2025TASK3StereoPSELDMamba。
原文摘要 · Abstract (English)
Pre-training methods have greatly improved the performance of sound event localization and detection (SELD). However, existing Transformer-based models still face high computational cost. To solve this problem, we present a stereo SELD system using a pre-trained PSELDnet and a bidirectional Mamba sequence model. Specifically, we replace the Conformer module with a BiMamba module. We also use asymmetric convolutions to better capture the time and frequency relationships in the audio signal. Test results on the DCASE2025 Task 3 development dataset show that our method performs better than both the baseline and the original PSELDnet with a Conformer decoder. In addition, the proposed model costs fewer computing resources than the baselines. These results show that the BiMamba architecture is effective for solving key challenges in SELD tasks. The source code is publicly accessible at https://github.com/ alexandergwm/DCASE2025 TASK3 Stereo PSELD Mamba.
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