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This thesis presents a method to visually detect and recognize fingering gestures of the left hand of a guitarist. The choice of computer vision to perform that task is motivated by the absence of a satisfying method for realtime guitarist fingering detection. The development of this computer vision method follows preliminary manual and automated analyses of video recordings of a guitarist. These first analyses led to some important findings about the design methodology of such a system, namely the focus on the effective gesture, the consideration of the action of each individual finger, and a recognition system not relying on comparison against a knowledge-base of previously learned fingering positions. Motivated by these results, studies on three important aspects of a complete fingering system were conducted. One study was on realtime finger-localization, another on string and fret detection, and the last on movement segmentation. Finally, these concepts were integrated into a prototype and a system for left-hand fingering detection was developed. Such a data acquisition system for fingering retrieval has uses in music theory, music education, automatic music and accompaniment generation and physical modeling.

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IDMIL Participants:

• Anne-Marie Burns

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AnneMarieBurns_MAthesis