🎸 Chord Classification 🚀 Project Overview This project is a deep learning-based chord classification system that identifies musical chords from audio recordings. By leveraging Convolutional Neural Networks (CNNs) and Chroma feature extraction, the model can accurately recognize chords from WAV files.

✨ Features The dataset is created by myself Supports a wide range of chords: major, minor, diminished, augmented, 7th, and more. Advanced audio processing: Uses Chroma feature extraction for precise chord identification. Deep learning-powered: Built with Python, TensorFlow/Keras, and Librosa. User-friendly interface: Interactive UI with Streamlit for real-time predictions. Customizable and extensible: Train the model with additional chord samples for improved accuracy.

📂 Project Structure

📦 chord-classification ┣ 📂 dataset/ # Chord audio samples
┣ 📂 models/ # Trained CNN models
┣ 📂 scripts/ # Feature extraction & training scripts
┣ 📂 docs/ # Documentation and references
┣ 📜 app.py # Streamlit UI for chord classification
┣ 📜 model.py # CNN model implementation
┣ 📜 requirements.txt # Dependencies
┗ 📜 README.md # Project documentation

📊 Model Performance

Training Accuracy: 87%

Test Accuracy: 77%

Note: Performance metrics will improve with a larger dataset.

🛠 How It Works Feature Extraction: Converts audio files into chroma spectrograms. CNN Model: Classifies the chord based on the extracted features. Interactive UI: Users upload audio files and receive real-time predictions. 📌 Future Improvements Expand dataset for better generalization. Implement real-time audio input processing. Optimize the CNN model for faster inference.

MADE WITH LOVE BY KAVISH :)❤