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COGNITIVE APPROACH TO MUSICAL DATA ANALYSIS

  • Bożena Kostek
Chapter
  • 380 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 3)

Abstract

Digital signal processing is one of the most rapidly developing areas of science. With the explosive expansion of the Internet, the number of very demanding computer network users increases. Content analysis and searching for specific content are relatively new areas, and therefore new concepts and algorithms of processing them appear quite often. Currently there are no faultless solutions. Sound data analysis is connected with difficulties in analytical description as well as with significant redundancy characterized by high entropy included in the very type of information. Such characteristics also prevail for sound separation problems, hence the number of algorithms for sound separation from musical material. In addition, one should notice that there are some limitations regarding percussion sounds and other non-harmonic sources. Easy extraction of such sounds by means of existing algorithms is not possible. Therefore one could state that such instruments are a source of noise for an algorithm, which makes its operation more difficult, and decreases the accuracy and credibility of the result. Concurrently, one should select the musical material for analysis based on the instrumentation, avoiding non-harmonic sounds. In addition, the articulation such as glissando or tremolo causes the problem of detecting fundamental frequency in the spectrum. Another important factor should also be considered: the music of Western culture is based on simple relations of frequency to fundamental frequency. Therefore it is an obvious consequence that harmonic tones overlap in the spectrum, which makes the operation of most algorithms more difficult. The most basic notion in musical acoustics i.e. sound timbre, as mentioned in Chapter 2, remains unresolved, even if many important research works have been done in the field (e.g. McAdams et al 1995; Cosi et al 1994; Grey 1997; Krimphoff et al 1994; De Poli and Prandoni 1997; Toiviainen et al 1998, Wessel 1979).

Keywords

Independent Component Analysis Flow Graph Cognitive Approach Independent Component Analysis Algorithm Musical Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  • Bożena Kostek
    • 1
  1. 1.Multimedia Systems Department Faculty of ElectronicsTelecommunications and Informatics Gdansk University of Technology ul. Narutowicza 11/1280-952 GdanskPoland

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