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Sound event localization and detection (SELD) is a combined task ofidentifying the sound event and its direction. Deep neural networks (DNNs) areutilized to associate them with the sound signals observed by a microphonearray. 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 whilewalking) require a wearable microphone array whose geometry can be designed tosuit the task. In this paper, for the development of such a wearable SELD, wepropose a dataset named Wearable SELD dataset. It consists of data recorded by24 microphones placed on a head and torso simulators (HATS) with someaccessories mimicking wearable devices (glasses, earphones, and headphones). Wealso provide experimental results of SELD using the proposed dataset andSELDNet to investigate the effect of microphone configuration.

Wearable SELD dataset: Dataset for sound event localization and detection using wearable devices around head