Amplifying a Sense of Emotion toward Drama- Long Short-Term Memory Recurrent Neural Network for dynamic emotion recognition

摘要

This paper tried to amplify a sense of emotion toward drama. Using Long Short-Term Memory Recurrent Neural Network to model and predict dynamic emotion(Arousal and Valence) recognition. After building model, we transplant whole framework and take results from it on visualizing. We have two demo version: RGB version and Vignette version. RGB version is to modulate the RGB value of frame in video. The Vignette one is to add the vignette effect. Both version all are to amplify a sense of emotion toward drama. Let people have more fun during watching videos. The database we used is NNIME (The NTHU-NTUA Chinese Interactive Multimodal Emotion Corpus) [1]. NNIME is a newly-collected multimodal corpus. This database includes recordings of 44 subjects engaged in spontaneous dyadic spoken interaction. The length of data is about 11 hours containing audio, video and electrocardiogram. The database is also completed with a rich set of emotion annotations on continuous-in-time annotation by four annotators. This carefixlly-engineered data collection and annotation processes provide us to create amplify framework.