Chordanalyzer

Abstract

Chord Analyzer is a web-based application designed to recognize guitar chords in real-time using webcam-based image processing. The objective of this project is to assist guitarists in identifying chords accurately by analyzing finger placements on the fretboard. This eliminates the need for audio-based recognition, making it a versatile tool for both beginners and experienced musicians. The project utilizes computer vision and machine learning techniques to detect and classify chords. OpenCV is used for image processing, and deep learning models are employed to recognize hand positions and fretboard markings. A convolutional neural network (CNN) is trained on a dataset of guitar chord images to enhance accuracy. The process begins with capturing a video frame via webcam. The system then applies image preprocessing techniques such as edge detection and contour analysis to isolate the fretboard and fingers. Feature extraction is performed to map the finger positions to possible chord formations. The trained CNN model then predicts the chord being played. Extensive testing has been conducted to improve recognition accuracy, achieving an 80% accuracy in controlled environments. The motivation behind this project is to bridge the gap between traditional music learning methods and modern AI-driven solutions. By providing an intuitive and interactive learning experience, Chord Analyzer aims to help musicians refine their skills and improve their efficiency in learning new chords.