Multi Event Localization by Audio-Visual Fusion with Omnidirectional Camera and Microphone Array

Abstract

Audio-visual fusion is a promising approach for identifying multiple events occurring simultaneously at different locations in the real world. Previous studies on audio-visual event localization (AVE) have been built on datasets that only have monaural or stereo channels in the audio; thus, it was hard to distinguish the direction of audio when different sounds are heard from multiple locations. In this paper, we develop a multi-event localization method using multichannel audio and omnidirectional images. To take full advantage of the spatial correlation between the features in the two modalities, our method employs early fusion that can retain audio direction and background information in images. We also created a new dataset of multi-label events containing around 660 omnidirectional videos with multichannel audio, which was used to showcase the effectiveness of the proposed method.