Artificial perception and music recognition /

Artificial perception and music recognition /

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Bibliographic Details
Author / Creator:	Tanguiane, Andranick S., 1952-
Imprint:	Berlin ; New York : Springer-Verlag, ©1993.
Description:	1 online resource (xiv, 210 pages) : illustrations.
Language:	English
Series:	Lecture notes in computer science, 0302-9743 ; 746. Lecture notes in artificial intelligence Lecture notes in computer science ; 746. Lecture notes in computer science. Lecture notes in artificial intelligence.
Subject:	Artificial intelligence -- Musical applications. Computer sound processing. Musical notation -- Data processing. Artificial intelligence -- Musical applications. Computer sound processing. Musical notation -- Data processing.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11071830

Hidden Bibliographic Details
ISBN:	9783540481270 3540481273 3540573941 9783540573944 0387573941 9780387573946
Notes:	Includes bibliographical references (pages 185-200) and indexes. Online resource; title from PDF title page (SpringerLink, viewed Oct. 9, 2013).
Summary:	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.
Other form:	Print version: Tanguiane, Andranick S., 1952- Artificial perception and music recognition. Berlin ; New York : Springer-Verlag, ©1993 3540573941

Description
Summary:	This monograph presents the author's studies in musicrecognition aimed at developing a computer system forautomatic notation of performed music. The performance ofsuch a system is supposed to be similar to that of speechrecognition systems: acoustical data at the input and musicscoreprinting at the output.The approach to pattern recognition employed is thatofartificial perception, based on self-organizing input datain order to segregate patterns before their identificationby artificial intelligencemethods. The special merit of theapproach is that it finds optimal representations of datainstead of directly recognizing patterns.
Physical Description:	1 online resource (xiv, 210 pages) : illustrations.
Bibliography:	Includes bibliographical references (pages 185-200) and indexes.
ISBN:	9783540481270 3540481273 3540573941 9783540573944 0387573941 9780387573946
ISSN:	0302-9743 ;