EgoMusic: An Egocentric Augmented Reality Glasses Dataset for Music

摘要

Although audio-augmented reality (AAR) has known applications in music, the use of wearables such as augmented reality (AR) glasses for egocentric audio data capture for music has not been investigated. Current egocentric datasets are mostly focused on speech research, neglecting music's unique demands for tasks such as real-time optimisation or assistive listening. This paper introduces EgoMusic, a multimodal dataset featuring synchronised egocentric audio-visual data captured with AR glasses during live performances, alongside studio-quality audio references. We investigate AR glasses' utility for music and baseline artificial intelligence (AI) approaches for hearing enhancement, positioning EgoMusic as the first dataset that enables research for egocentric music AAR.