Artificial Perception and Music Recognition [electronic resource] /
edited by Andranick S. Tanguiane.
- Berlin, Heidelberg : Springer Berlin Heidelberg, 1993.
- XV, 210 p. online resource.
- Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, 746 0302-9743 ; .
- Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, 746 .
Correlativity of perception -- Substantiating the model -- Implementing the model -- Experiments on chord recognition -- Applications to rhythm recognition -- Applications to music theory -- General discussion -- Conclusions.
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.
9783540481270
10.1007/BFb0019384 doi
Computer science.
Artificial intelligence.
Optical pattern recognition.
Telecommunication.
Computer Science.
Artificial Intelligence (incl. Robotics).
Pattern Recognition.
Data Storage Representation.
Communications Engineering, Networks.
Q334-342 TJ210.2-211.495
006.3
Correlativity of perception -- Substantiating the model -- Implementing the model -- Experiments on chord recognition -- Applications to rhythm recognition -- Applications to music theory -- General discussion -- Conclusions.
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.
9783540481270
10.1007/BFb0019384 doi
Computer science.
Artificial intelligence.
Optical pattern recognition.
Telecommunication.
Computer Science.
Artificial Intelligence (incl. Robotics).
Pattern Recognition.
Data Storage Representation.
Communications Engineering, Networks.
Q334-342 TJ210.2-211.495
006.3