MTDA-HSED：面向异构声音事件检测的互助调优与双分支聚合
MTDA-HSED: Mutual-Assistance Tuning and Dual-Branch Aggregating for Heterogeneous Sound Event Detection
AI总结：
针对异构声音事件检测中复杂场景特征学习不足的问题，提出含M3A适配器和DBMF模块的MTDA-HSED双分支架构，在DESED等数据集上mpAUC超基线5%。
摘要：
声音事件检测（SED）在理解与感知声学场景中发挥着关键作用。现有方法已展现出出色的性能，但在从异构数据集中学习复杂场景的特征方面存在不足。本文提出了一种名为MTDA-HSED（面向异构声音事件检测的互助调优与双分支聚合）的新型双分支架构。该架构采用互助音频适配器（M3A）有效应对多场景问题，使用双分支中间融合（DBMF）模块解决多粒度问题。具体而言，M3A作为适配器集成到BEATs模块中，通过在多场景数据集上微调来提升BEATs的性能。DBMF模块连接BEATs与CNN分支，促进二者信息的深度融合。实验结果表明，所提方法在DESED和MAESTRO Real数据集上的mpAUC指标超出基线5%。代码可在https://github.com/Visitor-W/MTDA获取。
英文摘要：
Sound Event Detection (SED) plays a vital role in comprehending and perceiving acoustic scenes. Previous methods have demonstrated impressive capabilities. However, they are deficient in learning features of complex scenes from heterogeneous dataset. In this paper, we introduce a novel dual-branch architecture named Mutual-Assistance Tuning and Dual-Branch Aggregating for Heterogeneous Sound Event Detection (MTDA-HSED). The MTDA-HSED architecture employs the Mutual-Assistance Audio Adapter (M3A) to effectively tackle the multi-scenario problem and uses the Dual-Branch Mid-Fusion (DBMF) module to tackle the multi-granularity problem. Specifically, M3A is integrated into the BEATs block as an adapter to improve the BEATs' performance by fine-tuning it on the multi-scenario dataset. The DBMF module connects BEATs and CNN branches, which facilitates the deep fusion of information from the BEATs and the CNN branches. Experimental results show that the proposed methods exceed the baseline of mpAUC by \textbf{$5\%$} on the DESED and MAESTRO Real datasets. Code is available at https://github.com/Visitor-W/MTDA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
