Emotion Detection Cpc

Emotion detection in audio utilising self-supervised representations trained with Contrastive Predictive Coding (CPC).

AI & Machine LearningPythonMIT

Abstract

Emotion Detection Cpc is an open-source AI & Machine Learning project. Emotion detection in audio utilising self-supervised representations trained with Contrastive Predictive Coding (CPC). This repo provides the code for an emotion recognition system using speech as an input. The performance is boosted using self-supervised representations trained with Contrastive Predictive Coding (CPC). It is built using Python, Machine Learning, PyTorch. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

This repo provides the code for an emotion recognition system using speech as an input. The performance is boosted using self-supervised representations trained with Contrastive Predictive Coding (CPC). Results have improved from a baseline of 71% to 80% accuracy when using CPC which is a significant relative reduction in error of 30%.

2. Objective

Emotion detection in audio utilising self-supervised representations trained with Contrastive Predictive Coding (CPC).

This project demonstrates how Python, Machine Learning, PyTorch can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

PythonMachine LearningPyTorch

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/jplhughes/emotion_detection_cpc.git
cd emotion_detection_cpc
virtualenv -p python3.7 venv
source venv/bin/activate
make deps

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by jplhughes and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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