Multimodal Emotion Recognition

# Multimodal Emotion Recognition ## Description This project explores the hypothesis that large-scale multimodal correspondence learning can enhance the performance of real-time audiovisual emotion recognition systems. ## Research Hypothesis Applying large-scale multimodal correspondence learning can improve real-time emotion recognition by processing audiovisual inputs effectively. ## Implementation Approach The project uses PE-AV encoders for processing audiovisual inputs from a live video feed, classifying emotions using a labeled dataset. The system's performance will be evaluated in terms of accuracy and response time. ## Setup Instructions 1. Clone the repository: ```bash git clone cd multimodal_emotion_recognition ``` 2. Install the required dependencies: ```bash pip install -r requirements.txt ``` 3. Download and prepare the dataset (RAVDESS, CREMA-D) into the `data/` directory. ## Usage Examples Run the training script: ```bash python src/train.py ``` Run the real-time emotion recognition system: ```bash python src/realtime.py ``` ## Expected Results The system should accurately classify emotions in real-time audiovisual inputs with improved performance over existing systems. ## References - Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning: [arXiv:2512.19687v1](http://arxiv.org/abs/2512.19687v1)