  Memories are the tethering threads that tie us to the world, and memorability
is the measure of their tensile strength. The threads of memory are spun from
fibres of many modalities, obscuring the contribution of a single fibre to a
thread's overall tensile strength. Unfurling these fibres is the key to
understanding the nature of their interaction, and how we can ultimately create
more meaningful media content. In this paper, we examine the influence of audio
on video recognition memorability, finding evidence to suggest that it can
facilitate overall video recognition memorability rich in high-level (gestalt)
audio features. We introduce a novel multimodal deep learning-based late-fusion
system that uses audio gestalt to estimate the influence of a given video's
audio on its overall short-term recognition memorability, and selectively
leverages audio features to make a prediction accordingly. We benchmark our
audio gestalt based system on the Memento10k short-term video memorability
dataset, achieving top-2 state-of-the-art results.
