Semi-Automatic mulsemedia authoring analysis from the user's perspective

Abstract

Mulsemedia (Multiple Sensorial Media) authoring is a complex task that requires the author to scan the media content to identify the moments to activate sensory effects. A novel proposal is to integrate content recognition algorithms into authoring tools to alleviate the authoring effort. Such algorithms could potentially replace the work of the human author when analyzing audiovisual content, by performing automatic extraction of sensory effects. Besides that, the semi-Automatic method proposes to maintain the author subjectivity, allowing the author to define which sensory effects should be automatically extracted. This paper presents an evaluation of the proposed semi-Automatic authoring considering the point of view of users. Experiments were done with the STEVE 2.0 mulsemedia authoring tool. Our work uses the GQM (Goal Question Metric) methodology, a questionnaire for collecting users' feedback, and analyzes the results. We conclude that users believe that the semi-Automatic authoring is a positive addition to the authoring method.