Using High-Level Information to Detect Key Audio Events in a Tennis Game

This paper describes how the detection of key audio events in a sports game (tennis) can be enhanced by the use of high-level information. High-level features are able to provide useful constraints on the detection procedure, and thus to improve detection performance. We define two types of event based information: event dependency and inter-event timing. These respectively characterize the identity of the next event and the time at which the next event will occur. Probabilistic models of high-level constraints are developed, and then integrated into our event detection framework. We test this approach on audio tracks extracted from two different tennis games. The results show that significant improvements in both accuracy and computational efficiency are obtained when applying high-level information.