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https://github.com/shivamratti13/audio-emotion-recognition
Developed an audio emotion recognition system using the RAVDESS dataset, classifying audio into 8 emotional categories with 77.59% accuracy. Built a Streamlit app for real-time classification via audio upload or recording. Leveraged Librosa for feature extraction (MFCCs) and a Random Forest classifier, creating a robust and user-friendly solution.
https://github.com/shivamratti13/audio-emotion-recognition
audio audio-processing classification ensemble-learning jupyter-notebook machine-learning python random-forest-classifier streamlit
Last synced: about 1 month ago
JSON representation
Developed an audio emotion recognition system using the RAVDESS dataset, classifying audio into 8 emotional categories with 77.59% accuracy. Built a Streamlit app for real-time classification via audio upload or recording. Leveraged Librosa for feature extraction (MFCCs) and a Random Forest classifier, creating a robust and user-friendly solution.
- Host: GitHub
- URL: https://github.com/shivamratti13/audio-emotion-recognition
- Owner: shivamratti13
- Created: 2025-01-15T11:04:13.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-01-15T11:56:39.000Z (about 1 month ago)
- Last Synced: 2025-01-15T13:14:13.910Z (about 1 month ago)
- Topics: audio, audio-processing, classification, ensemble-learning, jupyter-notebook, machine-learning, python, random-forest-classifier, streamlit
- Language: Jupyter Notebook
- Homepage: https://audio-emotion-recognition-s.streamlit.app/
- Size: 433 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0