This monograph presents the author's studies in music recognition aimed at developing a computer system for automatic notation of performed music. The performance of such a system is supposed to be similar to that of speech recognition systems: acoustical data at the input and music scoreprinting at the output. The approach to pattern recognition employed is thatof artificial perception, based on self-organizing input data in order to segregate patterns before their identification by artificial intelligencemethods. The special merit of the approach is that it finds optimal representations of data instead of directly recognizing patterns.
Author(s): Andranick S. Tanguiane (eds.)
Series: Lecture Notes in Computer Science 746 : Lecture Notes in Artificial Intelligence
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 1993
Language: English
Pages: 210
City: Berlin; New York
Tags: Artificial Intelligence (incl. Robotics); Pattern Recognition; Data Storage Representation; Communications Engineering, Networks
Introduction....Pages 1-26
Correlativity of perception....Pages 27-44
Substantiating the model....Pages 45-76
Implementing the model....Pages 77-94
Experiments on chord recognition....Pages 95-130
Applications to rhythm recognition....Pages 131-152
Applications to music theory....Pages 153-176
General discussion....Pages 177-180
Conclusions....Pages 181-183
