🎵 Audio Signal Emotion Classifier 🎧

Classify Emotions From Voice Using Machine Learning (Random Forest, SVM, XGBoost, GNB)

🚀 Live Demo

🔗 Try it on Streamlit Cloud

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💡 Overview

This Streamlit web app allows you to classify emotions from WAV audio files using machine learning models like:

• 🌳 Random Forest
• 🔥 XGBoost
• 💡 Gaussian Naive Bayes
• 💻 Support Vector Machine (SVM)

Simply upload your .wav file and see the magic happen. Complete with emotion predictions, waveform plots, spectrograms, and feature importance charts!

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🔍 Supported Emotions

The classifier is trained on labeled emotion data with the following categories:

• 😞 Disappointed
• 😖 Disgust
• 😄 Happy
• 😐 Neutral
• 😲 Surprise

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🧠 Features

✅ Model Selector: Choose from RF, SVM, GNB, or XGB
⚙️ Parameter Option: Use Default or Tuned models
📈 Audio Visualization: Waveform and Spectrogram
📊 Feature Importance: Only for Random Forest
🔉 Audio Preview: Listen before predicting

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📁 How to Run Locally

1. Clone this repository:

   git clone https://github.com/fbrianzy/emotion-classifier-using-audio
   cd emotion-classifier-using-audio

2. Pip Install Requirements Dependencies

   pip install -r requirements.txt

3. Run Streamlit Code

   streamlit run main_app.py