Using an electric bass guitar as a case study, one goal of our system is to accelerate the process of associating a musical score, sounds represented by that score and the fingerboard. We present a system that assists beginner level musicians in learning of electric bass guitar. The experimental result demonstrates that this fingertip tracking algorithm is robust enough for tracking fingertips (1) without any constrains such us color marker (2) under the complex contexts, such us complicated background, different illumination conditions, (3) with the high tracking accuracy (mean error 3.36 pixels for four fingertips). Finally, particles are distributed only between the associated fingertip candidates to track the fingertips of guitarist effectively. Then, a data association algorithm is utilized to associate 4 tracked fingers (index finger, middle finger, ring finger, little finger) with their correspondent tracked results frame by frame. Furthermore, a temporal Grouping algorithm, which is a geometry analysis for consecutive frames, is applied to removal noise and group the same fingertips (index finger, middle finger, ring finger, little finger).

Then, the probability map of fingertip is generated on the segmentation results by counting the voting numbers of the Template Matching and Reversed Hough Transform. First a machine learning-based Bayesian Pixel Classifier is used to segment the hand area on the test data. This paper proposes a temporal Grouping and pattern analysis-based algorithm that could track the fingertips of guitarists during their guitar playing towards the actualization of the automatic guitar fingering recognition system. The system runs and displays tablature in real time, making it especially useful for educational purposes. The approach achieves a significantly higher accuracy than similar systems described in the literature. We use this information to generate tablature notation, a guitar specific notation that shows which strings and frets should be played for every beat.

We track the guitar’s strings and frets, and in each frame we use skin detection to localize the guitarist’s fingers on the fretboard. Our approach is vision based, yet markerless. Most guitar transcription systems to date rely on machine learning, specialized gear, or drawn-on markers. In this paper we present a system that uses a vision-based approach to generate musical notation for guitar.

Vision-based approaches can address this by tracking the musician’s gestures, and generating the score based on the way the musician plays the instrument. However, extracting pitch from an audio recording can be difficult.

Traditionally, automatic music transcription uses audio recordings to generate the score of a musical piece.

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