Exemplar-based Learning in Adaptive Optical Music Recognition System
نویسنده
چکیده
The learning process of an adaptive optical music recognition system (AOMR) is described here. By combining k-nearest neighbor classifier and genetic algorithm, the system can learn to recognize new music symbols and handwritten music notations, and it also continually improves the accuracy in recognizing these objects. Given the wide range of music notation styles, these are essential characteristics of a music recognizer.
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تاریخ انتشار 1996