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

TL;DR

This paper introduces the Wearable SELD dataset, a new resource for sound event localization and detection using wearable microphone arrays around the head, enabling research in real-world wearable scenarios.

Contribution

The paper presents a novel wearable microphone array dataset for SELD, including data collection and analysis of different microphone configurations with SELDNet.

Findings

Microphone placement affects SELD performance

Wearable microphone array data is viable for SELD tasks

Experimental results demonstrate the dataset's usefulness

Abstract

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.