{"id":26166736,"url":"https://github.com/vamshikrishna779/emotionsense","last_synced_at":"2026-04-06T08:31:29.936Z","repository":{"id":277673482,"uuid":"933164722","full_name":"Vamshikrishna779/EmotionSense","owner":"Vamshikrishna779","description":"Speech Emotion Detection using Machine Learning This project detects emotions from speech audio using machine learning. It extracts features, trains classifiers, and employs an ensemble model for accuracy. 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The system extracts features from audio recordings, trains multiple classifiers, and utilizes an ensemble model to enhance accuracy. Additionally, a **Flask web application** has been developed to provide a user-friendly interface for emotion detection.\n\n## Features\n- **Audio-based Emotion Recognition**: Classifies speech into seven emotion categories.\n- **Machine Learning Models**: Uses various classifiers to determine the most effective model.\n- **Ensemble Learning**: Combines top-performing models for improved accuracy.\n- **Flask Web App**: Provides a user interface for uploading audio files and getting predictions.\n- **Data Visualization**: Displays feature distributions and model performance analysis.\n\n## Technologies Used\n- **Python**\n- **Machine Learning Libraries**: Scikit-learn, NumPy, Pandas\n- **Audio Processing**: Librosa\n- **Web Development**: Flask, HTML, CSS, JavaScript\n- **Model Persistence**: Joblib\n\n## Dataset \u0026 Feature Extraction\n- The dataset contains audio files categorized by emotions:\n  - `OAF_Fear`, `OAF_Pleasant_surprise`, `OAF_Sad`, `OAF_angry`, `OAF_disgust`, `OAF_happy`, `OAF_neutral`.\n- Features extracted: **MFCC (Mel Frequency Cepstral Coefficients)**.\n- The extracted dataset is structured as a `(1400, 13)` feature matrix.\n\n## Machine Learning Models\n| Model                   | Accuracy (%) |\n|-------------------------|-------------|\n| RandomForest           | 91.43       |\n| SVM                    | 87.86       |\n| Logistic Regression    | 85.00       |\n| K-Nearest Neighbors    | 86.79       |\n| Decision Tree          | 77.86       |\n| Naive Bayes            | 86.43       |\n| Gradient Boosting      | 87.86       |\n\n## Ensemble Model - `EmotionEnsembleClassifier`\n- Combines **RandomForest, SVM, and Gradient Boosting** using majority voting.\n- Achieved **91.07% accuracy**.\n\n## Flask Web Application\n- Allows users to upload audio files for real-time emotion detection.\n- Backend processes audio, extracts MFCC features, and predicts emotion using the trained ensemble model.\n- Built using **Flask, HTML, CSS, and JavaScript**.\n\n## Installation \u0026 Setup\n### 1. Clone the Repository\n```sh\ngit clone https://github.com/your-username/speech-emotion-detection.git\ncd speech-emotion-detection\n```\n### 2. Create a Virtual Environment\n```sh\npython -m venv venv\nsource venv/bin/activate  # Windows: venv\\Scripts\\activate\n```\n### 3. Install Dependencies\n```sh\npip install -r requirements.txt\n```\n### 4. Run the Flask App\n```sh\npython app.py\n```\n### 5. Access the Web Interface\nOpen your browser and go to: `http://localhost:5000/`\n\n## Project Structure\n```\n├── app.py                # Flask application\n├── models/               # Saved machine learning models\n├── static/               # CSS, JavaScript files\n├── templates/            # HTML files for web UI\n├── data/                 # Dataset and processed features\n├── requirements.txt      # Dependencies\n├── README.md            # Documentation\n```\n\n## Future Enhancements\n- Implement **Deep Learning (CNN/LSTM)** for better feature extraction.\n- Improve **real-time processing** for live speech input.\n- Enhance **dataset diversity** to cover more emotions.\n\n## Contributors\n- **G Vamshikrishna** - [GitHub](https://github.com/Vamshikrishna779)\n\n## Contact\nFor any queries or collaboration opportunities:\n- **GitHub**: [Vamshikrishna779](https://github.com/Vamshikrishna779)\n\n## License\nThis project is licensed under the **MIT License**.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvamshikrishna779%2Femotionsense","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvamshikrishna779%2Femotionsense","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvamshikrishna779%2Femotionsense/lists"}