Improved audio event detection by use of contextual noise

This paper presents new approaches to improve the detection of two key audio events in a sport game (tennis) using contextual information. When analysing a tennis match using only audio information, the sound of the ball being struck and the occurrence of a line judge´s shout can be obscured by players´ grunts or shouts. Furthermore, if models of these two important events are trained from labelled training-data, there is often considerable audio mis-match with the test-data, which means that detection performance can be very poor. To handle this problem, we regard the players´ grunts as useful contextual information that indicates the position of the events of interest. We show how to use an unsupervised learning method to build an improved model of the ball-hit event using grunt information. We can then use high-level information to distinguish grunts from line-judge shouts. This approach gives simultaneous improvements in detection of both ball-hits and line judge shouts, and is portable between different matches, unlike approaches based on the use of manually labelled training-data.