{"ID":2838191,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17926","arxiv_id":"2511.17926","title":"Three-Class Emotion Classification for Audiovisual Scenes Based on Ensemble Learning Scheme","abstract":"Emotion recognition plays a pivotal role in enhancing human-computer interaction, particularly in movie recommendation systems where understanding emotional content is essential. While multimodal approaches combining audio and video have demonstrated effectiveness, their reliance on high-performance graphical computing limits deployment on resource-constrained devices such as personal computers or home audiovisual systems. To address this limitation, this study proposes a novel audio-only ensemble learning framework capable of classifying movie scenes into three emotional categories: Good, Neutral, and Bad. The model integrates ten support vector machines and six neural networks within a stacking ensemble architecture to enhance classification performance. A tailored data preprocessing pipeline, including feature extraction, outlier handling, and feature engineering, is designed to optimize emotional information from audio inputs. Experiments on a simulated dataset achieve 67% accuracy, while a real-world dataset collected from 15 diverse films yields an impressive 86% accuracy. These results underscore the potential of audio-based, lightweight emotion recognition methods for broader consumer-level applications, offering both computational efficiency and robust classification capabilities.","short_abstract":"Emotion recognition plays a pivotal role in enhancing human-computer interaction, particularly in movie recommendation systems where understanding emotional content is essential. While multimodal approaches combining audio and video have demonstrated effectiveness, their reliance on high-performance graphical computing...","url_abs":"https://arxiv.org/abs/2511.17926","url_pdf":"https://arxiv.org/pdf/2511.17926v1","authors":"[\"Xiangrui Xiong\",\"Zhou Zhou\",\"Guocai Nong\",\"Junlin Deng\",\"Ning Wu\"]","published":"2025-11-22T05:54:30Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.HC\"]","methods":"[]","has_code":false}