  We live in a world filled with never-ending streams of multimodal
information. As a more natural recording of the real scenario, long form
audio-visual videos are expected as an important bridge for better exploring
and understanding the world. In this paper, we propose the multisensory
temporal event localization task in long form videos and strive to tackle the
associated challenges. To facilitate this study, we first collect a large-scale
Long Form Audio-visual Video (LFAV) dataset with 5,175 videos and an average
video length of 210 seconds. Each of the collected videos is elaborately
annotated with diversified modality-aware events, in a long-range temporal
sequence. We then propose an event-centric framework for localizing
multisensory events as well as understanding their relations in long form
videos. It includes three phases in different levels: snippet prediction phase
to learn snippet features, event extraction phase to extract event-level
features, and event interaction phase to study event relations. Experiments
demonstrate that the proposed method, utilizing the new LFAV dataset, exhibits
considerable effectiveness in localizing multiple modality-aware events within
long form videos. Project website: http://gewu-lab.github.io/LFAV/
