论文标题
声音事件检测和分离：基于以下合成音景的基准
Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes
论文作者
论文摘要
我们提出了最先进的声音事件检测系统（SED）的基准。我们设计了合成评估集，以关注特定的声音事件检测挑战。我们根据与时间相关的修改（事件的时间位置和剪辑的时间位置）分析了提交对DCASE 2021任务4的表现，并研究了非目标声音事件和混响的影响。我们表明，声音事件时期的本地化仍然是SED系统的问题。我们还表明，混响和非目标声音事件正在严重降低SED系统的性能。在后一种情况下，声音分离似乎是一个有前途的解决方案。
We propose a benchmark of state-of-the-art sound event detection systems (SED). We designed synthetic evaluation sets to focus on specific sound event detection challenges. We analyze the performance of the submissions to DCASE 2021 task 4 depending on time related modifications (time position of an event and length of clips) and we study the impact of non-target sound events and reverberation. We show that the localization in time of sound events is still a problem for SED systems. We also show that reverberation and non-target sound events are severely degrading the performance of the SED systems. In the latter case, sound separation seems like a promising solution.
