Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach.

The aim of this paper is to reduce the effect of mismatch in recording conditions due to the transmission channel and recording device, using conditional dependencies of prosodic and spectral envelope features. The developed system is based on a Bayesian network framework which combines statistical models of the pitch and spectral envelope features. This approach is applied to forensic automatic speaker recognition, where mismatched recording conditions pose a serious problem to the accurate estimation of the strength of voice evidence. The method is evaluated using a forensic speaker recognition database that contains three different recording conditions typical to forensic tasks. The performance of the system is evaluated using both speaker verification as well as forensic speaker recognition measures.