Music Emotion Recognition-Based Business-Oriented Visualization Framework Using AI-driven Serverless Cloud Computing

This paper proposes a novel framework for a real-time music visualization system designed for the hearing-impaired, utilizing AI and serverless computing. The system converts audio signals into visual representations that capture both the physical and emotional aspects of music. A neural network-based Music Emotion Recognition (MER) model extracts emotional cues, which are integrated into the visualizations. The serverless computing ensures accessibility, while an account management system and comment collection system enable customization and regular retraining of the model for better accuracy. Results demonstrate the framework's effectiveness, highlighting the scalability and cost-efficiency of serverless computing. This work significantly advances music accessibility for the hearing-impaired, enhancing sensory experiences and promoting mental well- being. The MER model shows superior performance, with a 46.8% lower Root Mean Squared Error (RMSE) compared to other works targeting the same 10-second audio fragment length and a 13.5% higher Pearson's correlation coefficient (PCC) for 30-second fragments.