Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153490
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dc.contributor.authorChaudhary, Nitesh Kumaren_US
dc.date.accessioned2021-12-06T05:19:42Z-
dc.date.available2021-12-06T05:19:42Z-
dc.date.issued2021-
dc.identifier.citationChaudhary, N. K. (2021). Multimodal audio-visual emotion detection. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153490en_US
dc.identifier.urihttps://hdl.handle.net/10356/153490-
dc.description.abstractAudio and visual utterances in video are temporally and semantically dependent to each other so modeling of temporal and contextual characteristics plays a vital role in understanding of conflicting or supporting emotional cues in audio-visual emotion recognition (AVER). We introduced a novel temporal modelling with contextual features for audio and video hierarchies to AVER. To extract abstract temporal information, we first build temporal audio and visual sequences that are then fed into large Convolutional Neural Network (CNN) embeddings. We trained a recurrent network to capture contextual semantics from temporal interdependencies of audio and video streams by using the abstract temporal information. The encapsulated AVER approach is end-to-end trainable and enhances the state-of-art accuracies with a greater margin.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleMultimodal audio-visual emotion detectionen_US
dc.typeThesis-Master by Researchen_US
dc.contributor.supervisorJagath C Rajapakseen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeMaster of Engineeringen_US
dc.identifier.doi10.32657/10356/153490-
dc.contributor.supervisoremailASJagath@ntu.edu.sgen_US
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