基于共享权重与注意力机制的ResNet-Conformer模型，实现音频中声音事件的定位、检测与距离估计。
Resnet-conformer network with shared weights and attention mechanism for sound event localization, detection, and distance estimation
- 融合ResNet与Conformer结构，共享权重并引入注意力机制提升特征表达。
- 在开发集测试上达到40.2%的F-score、17.7度方向误差和0.32的相对距离误差。
- 适用于真实场景下的声学事件分析，尤其适合环境感知与机器人导航任务。
本文介绍了我们在DCASE 2024任务3A中的方法，聚焦于声音事件定位与检测（SELD）。SELD可为环境推断、导航等机器认知任务提供关键信息。今年挑战赛评估音频仅（赛道A）或音视频（赛道B）输入，在真实声景标注数据上进行测试。本年度新增距离估计任务，评估指标相应调整以实现全面评估。本提交针对赛道A，即音频仅任务。我们采用对数梅尔谱图与强度向量作为输入，并应用多种数据增强策略。提出基于EINV2的网络架构，在开发数据集测试集上取得40.2%的F-score、17.7度的方位角误差（DOA）以及0.32的相对距离误差（RDE）。
原文摘要 · Abstract (English)
This technical report outlines our approach to Task 3A of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024, focusing on Sound Event Localization and Detection (SELD). SELD provides valuable insights by estimating sound event localization and detection, aiding in various machine cognition tasks such as environmental inference, navigation, and other sound localization-related applications. This year's challenge evaluates models using either audio-only (Track A) or audiovisual (Track B) inputs on annotated recordings of real sound scenes. A notable change this year is the introduction of distance estimation, with evaluation metrics adjusted accordingly for a comprehensive assessment. Our submission is for Task A of the Challenge, which focuses on the audio-only track. Our approach utilizes log-mel spectrograms, intensity vectors, and employs multiple data augmentations. We proposed an EINV2-based [1] network architecture, achieving improved results: an F-score of 40.2%, Angular Error (DOA) of 17.7 degrees, and Relative Distance Error (RDE) of 0.32 on the test set of the Development Dataset [2 ,3].
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