Three-Class Emotion Classification for Audiovisual Scenes Based on Ensemble Learning Scheme
Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Xiong, Xiangrui, Zhou, Zhou, Nong, Guocai, Deng, Junlin, Wu, Ning
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7106677163
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