We develop an algorithm for the detection and classification of affective sound events underscored by specific patterns of sound energy dynamics. We relate the portrayal of these events to proposed high level affect or emotional coloring of the events. In this paper, four possible characteristic sound energy events are identified that convey well established meanings through their dynamics to partray and deliver certain affect, sentiment related to the horror film genre. Our algorithm is developed with the ultimate aim of auto-ox& ally strocruring sections of films that contain distinct shades of emotion related to horror themes for nonlinear media access and navigation. An average of 82% of the energy events, obtained from the analysis of the audio tracks of sections of four sample films corresponded correctly to the proposed affect. While the discrimination between certain sound energy event types was low, the algorithm correctly detected 71% of the occurences of the sound energy events within audio tracks of the films analyzed, and thus forms a useful basis for determining affective scenes characteristic
By: Simon Moncrief(Curtin University of Technology) , Chitra Dorai, Svetha Venkatesh(Curtin University of Technology)
Published in: RC22126 in 2001
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