论文标题
在未知环境中的声音事件本地化和检测的回声意识改编
Echo-aware Adaptation of Sound Event Localization and Detection in Unknown Environments
论文作者
论文摘要
我们的目标是开发一个声音事件的本地化和检测系统（SELD）系统，该系统在未知环境中稳健起作用。在未知环境中，在未知环境中培训的SELD系统由于环境效应（例如训练数据中未包含的回响和噪声）而被降低。先前对相关任务的研究表明，当在没有标签的情况下使用系统的数据时，域适应方法也有效。但是，适应未知环境仍然是一项艰巨的任务。在这项研究中，我们提出了SELD的Echo-Aware-Aware特征精炼（EAR），通过使用通过测量声音回声获得的未知环境的其他空间提示来抑制特征水平的环境效应。记录了包含100多个环境的脉冲响应数据集FOA-Meir，以验证提出的方法。 FOA-MEIR的实验表明，耳朵有效地改善了未知环境中的SELD性能。
Our goal is to develop a sound event localization and detection (SELD) system that works robustly in unknown environments. A SELD system trained on known environment data is degraded in an unknown environment due to environmental effects such as reverberation and noise not contained in the training data. Previous studies on related tasks have shown that domain adaptation methods are effective when data on the environment in which the system will be used is available even without labels. However adaptation to unknown environments remains a difficult task. In this study, we propose echo-aware feature refinement (EAR) for SELD, which suppresses environmental effects at the feature level by using additional spatial cues of the unknown environment obtained through measuring acoustic echoes. FOA-MEIR, an impulse response dataset containing over 100 environments, was recorded to validate the proposed method. Experiments on FOA-MEIR show that the EAR effectively improves SELD performance in unknown environments.
