🎸 Live Tonation Classification

Live Tonation Classification is a real-time key (tonality) detection system for musical audio, designed for musicians, music technologists, and audio hackers. It analyzes live audio input (e.g., from a guitar, keyboard, or microphone), detects the dominant notes, and predicts the musical key (major/minor) on the fly. The project features a modern Tkinter GUI for instant feedback and is built with modular, readable Python code.

---

🚀 Features

• Real-time audio streaming and processing
• Automatic key (tonality) detection (major/minor)
• Live note histogram for visualization and debugging
• Tkinter GUI for easy use and instant feedback
• Modular codebase: easy to extend, hack, or integrate
• Customizable detection window and analysis interval
• Cross-platform (Linux, Windows, macOS)

---

🖥️ GUI Preview

The app showing detected key and mode in real time.

---

🛠️ Installation

1. Clone the repository

git clone https://github.com/your-github-username/liveTonationClassification.git
cd liveTonationClassification

2. Install dependencies

pip install numpy sounddevice

(You may also need tkinter, which is included with most Python installations.)

---

🎹 Usage

Run the GUI application

python main.py

• Click Start Detection to begin analyzing audio from your default input device (microphone, audio interface, etc.).
• The detected key and mode will update live in the window.
• Click Stop Detection to end the session.

Run from terminal (no GUI)

python audio/stream.py

---

🧩 Project Structure

liveTonationClassification/
│
├── audio/
│   ├── stream.py           # Audio streaming & processing logic
│   ├── preprocessing.py    # Audio preprocessing utilities
│
├── model/
│   └── keyClassifier.py    # Core key detection logic
│
├── signalProcessing/
│   ├── pitchDetection.py   # Pitch detection from audio frames
│   └── fastFourierTransform.py # FFT utilities
│
├── windowApplication/
│   └── windowApplication.py # Tkinter GUI
│
├── main.py                 # GUI entry point
└── README.md

---

🧠 How it works

• Audio is streamed from your input device and split into frames.
• Pitch detection is performed on each frame using FFT.
• Detected notes are mapped to a histogram (C, C#, D, ..., B).
• Key classification uses custom templates for major/minor keys and a sliding window for robust detection.
• Results are displayed live in the GUI.

---

🎸 Example Use Cases

• Practice tool for musicians to check if they're staying in key
• Live music analysis for jam sessions or improvisation
• Educational tool for music theory classes
• Audio research and prototyping

🤝 Contributing

Pull requests, issues, and feature suggestions are welcome!
Feel free to fork and hack away.

---

📄 License

MIT License

---

🙏 Credits

• numpy
• sounddevice
• Tkinter

---

Made with ❤️ for live music and code.