{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/statistical-selection-of-cnn-based","title":"Statistical Selection of CNN-Based Audiovisual Features for Instantaneous Estimation of Human Emotional States","arxiv_id":"1708.07021","date":"2017-08-23","proceeding":null,"authors":["Ramesh Basnet","Mohammad Tariqul Islam","Tamanna Howlader","S. M. Mahbubur Rahman","Dimitrios Hatzinakos"],"abstract":"Automatic prediction of continuous-level emotional state requires selection\nof suitable affective features to develop a regression system based on\nsupervised machine learning. This paper investigates the performance of\nfeatures statistically learned using convolutional neural networks for\ninstantaneously predicting the continuous dimensions of emotional states.\nFeatures with minimum redundancy and maximum relevancy are chosen by using the\nmutual information-based selection process. The performance of frame-by-frame\nprediction of emotional state using the moderate length features as proposed in\nthis paper is evaluated on spontaneous and naturalistic human-human\nconversation of RECOLA database. Experimental results show that the proposed\nmodel can be used for instantaneous prediction of emotional state with an\naccuracy higher than traditional audio or video features that are used for\naffective computation.","url_abs":"http://arxiv.org/abs/1708.07021v1","url_pdf":"http://arxiv.org/pdf/1708.07021v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"statistical-selection-of-cnn-based","repo_url":"https://github.com/tariqul-islam/affectiveDimensionCNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}