  In this paper, we propose a study on multi-modal (audio and video) action
spotting and classification in soccer videos. Action spotting and
classification are the tasks that consist in finding the temporal anchors of
events in a video and determine which event they are. This is an important
application of general activity understanding. Here, we propose an experimental
study on combining audio and video information at different stages of deep
neural network architectures. We used the SoccerNet benchmark dataset, which
contains annotated events for 500 soccer game videos from the Big Five European
leagues. Through this work, we evaluated several ways to integrate audio stream
into video-only-based architectures. We observed an average absolute
improvement of the mean Average Precision (mAP) metric of $7.43\%$ for the
action classification task and of $4.19\%$ for the action spotting task.
