The Audio Emotion Recognition system utilizes convolutional neural networks (CNN) and FastAI to analyze and classify human emotions from audio inputs. Designed for real-time analysis, the application serves as a bridge between advanced machine learning techniques and practical applications.
Features and Technical Details
Model Architecture: The system relies on a CNN model fine-tuned on spectrograms to identify seven distinct emotions, achieving a validation accuracy of 91.8%.
Web Application: A React.js front-end provides a user-friendly interface for real-time audio emotion analysis, streamlining the user interaction process.
Backend Infrastructure: A Flask server supports the front-end, managing audio processing tasks and facilitating smooth communication with the deep learning model through a REST API.
Given its high accuracy and real-time feedback, this system can be integrated into various platforms like customer service interfaces, entertainment platforms, and mental health apps to derive insights from user emotions.