Determining computable scenes in films and their structures using audio-visual memory models
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Automatic Audio Content Analysis
Automatic Audio Content Analysis
Analysis of scene context related with emotional events
Proceedings of the tenth ACM international conference on Multimedia
Affective content detection using HMMs
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Proceedings of the 15th international conference on Multimedia
Personalized MTV Affective Analysis Using User Profile
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Emotion-based music recommendation by affinity discovery from film music
Expert Systems with Applications: An International Journal
Proceedings of the 5th Audio Mostly Conference: A Conference on Interaction with Sound
Personalization in multimedia retrieval: A survey
Multimedia Tools and Applications
Multimedia Tools and Applications
Affective classification in video based on semi-supervised learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
From motion to emotion: a wearable system for the multimedia enrichment of a Butoh dace performance
Journal of Mobile Multimedia
Affective content analysis of music video clips
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Hi-index | 0.00 |
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 portray and deliver certain affect, sentiment related to the horror film genre. Our algorithm is developed with the ultimate aim of automatically structuring 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 algotithm correctly detected 71% of the occurrences of the sound energy events within audio tracks of the films analyzed, and thus forms a useful basis for determining affective scenes characteristic of horror in movies.