论文标题
可穿戴式SELD数据集：用于声音事件的数据集定位和使用可穿戴设备周围的可穿戴设备
Wearable SELD dataset: Dataset for sound event localization and detection using wearable devices around head
论文作者
论文摘要
声音事件的本地化和检测（SELD）是确定声音事件及其方向的一项组合任务。深度神经网络（DNN）用于将它们与麦克风阵列观察到的声音信号相关联。尽管Ambisonic麦克风在SELD文献中很受欢迎，但由于其预定的几何形状，它们可能会限制应用的范围。一些应用程序（包括行人行走时执行静止的行人的应用程序）需要一个可穿戴的麦克风阵列，其几何形状可以设计以适应任务。在本文中，为了开发这种可穿戴设备，我们提出了一个名为可穿戴式数据集的数据集。它由24个麦克风录制的数据组成，并放置在头部和躯干模拟器上（帽子），其中一些配件模仿了可穿戴设备（眼镜，耳机和耳机）。我们还使用建议的数据集和SELDNET提供SELD的实验结果，以研究麦克风构型的效果。
Sound event localization and detection (SELD) is a combined task of identifying the sound event and its direction. Deep neural networks (DNNs) are utilized to associate them with the sound signals observed by a microphone array. Although ambisonic microphones are popular in the literature of SELD, they might limit the range of applications due to their predetermined geometry. Some applications (including those for pedestrians that perform SELD while walking) require a wearable microphone array whose geometry can be designed to suit the task. In this paper, for the development of such a wearable SELD, we propose a dataset named Wearable SELD dataset. It consists of data recorded by 24 microphones placed on a head and torso simulators (HATS) with some accessories mimicking wearable devices (glasses, earphones, and headphones). We also provide experimental results of SELD using the proposed dataset and SELDNet to investigate the effect of microphone configuration.
