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Artificial Perception and Music Recognition [electronic resource] / edited by Andranick S. Tanguiane.

Contributor(s): Material type: TextTextSeries: Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence ; 746Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1993Description: XV, 210 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783540481270
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q334-342
  • TJ210.2-211.495
Online resources:
Contents:
Correlativity of perception -- Substantiating the model -- Implementing the model -- Experiments on chord recognition -- Applications to rhythm recognition -- Applications to music theory -- General discussion -- Conclusions.
In: Springer eBooksSummary: 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.
Item type: E-BOOKS
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IMSc Library Link to resource Available EBK6326

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.

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The Institute of Mathematical Sciences, Chennai, India