Chord Detection Project

This project performs audio analysis and visualization, focusing on note recognition as a step towards chord detection. It utilizes Fast Fourier Transform (FFT) and chromagram techniques to analyze audio files and create visualizations.

Features

1. Waveform and Chromagram Visualization
2. Real-time Short-Time Fourier Transform (STFT) Animation
3. Peak Frequency Detection and Note Recognition
4. Audio-Video Synchronization

Requirements

• Python 3.x
• NumPy
• Matplotlib
• Librosa
• MoviePy

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/chord-detection-project.git
   cd chord-detection-project
   

2. Install the required packages:

   pip install -r requirements.txt
   

Usage

1. Place your audio file (WAV format) in the project directory.

2. Update the file_path variable in main.py with your audio file name:

   file_path = "your_audio_file.wav"

3. Run the script:

   python main.py
   

4. The script will generate:

   • A static plot showing the waveform and chromagram
   • An animated video of the STFT analysis
   • A final video with the STFT animation synchronized with the original audio

Future Development

This project is currently in the note recognition phase. Future developments will focus on implementing chord detection algorithms based on the recognized notes.

Some features to add:

• Better file-handling
• Chord recognition
• Improve this readme

License

This project is licensed under the MIT License.

Acknowledgments

This project uses the following libraries:

• Librosa for audio analysis
• Matplotlib for plotting and animation
• MoviePy for video editing

References:

1. "Design and Evaluation of a Simple Chord Detection Algorithm" by Christoph Hausner: http://www.fim.uni-passau.de/fileadmin/files/lehrstuhl/sauer/geyer/BA_MA_Arbeiten/BA-HausnerChristoph-201409.pdf