Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

Results found: 2

first rewind previous Page / 1 next fast forward last

Search results

Search:
in the keywords:  mutual information
help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
PL
Perception is a constructive mental process, which cannot be considered impersonally. Similarly, music cannot be cognised solely on the basis of its score, since its coming into being is strictly connected to the activation of human memory and sound imagination. The patterns that emerge from the sounds of heard music enable the listener to draw conclusions regarding the structures those sounds embody. However, such conclusions are accompanied by a degree of uncertainty, which concerns not just the perceived moment of the heard music, but also the way in which it is represented in the listener’s memory. Perception is an inferential, multi-layered, uncertain process, in which particular patterns seem more likely than others. Mental representations of those probabilities lie behind such essential musical phenomena as surprise, tension, expectation and pitch identification, which are fixed elements of theperception of music. The aim of the present article is to describe the essence of three selected types of music modelling, based on spectral anticipation (Shlomo Dubnov), based on memory (Rens Bod), and exploiting the dynamic character of music to obtain information (Samer Abdallah and Mark Plumbley). All these models take account of the element of uncertainty that accompanies the perception of music; hence they make use the foundations of information theory and statistical analysis as measurement ‘tools’. The use of these tools makes it possible to obtain numerical rates, which inform us of the degree of predictability of the musical structures being analysed. One crucial advantage of these methods is the possibility of evaluating them in respect to the use of real musical structures, deriving from actual music, and not abstract structures formed for the purposes of research. We obtain cognitive insight into the analysed music by employing methods of a mathematical provenance, and so we have the possibility of examining music whilst taking account of the role of the listener, but with the use of objectivised methods.
EN
A presence of a noise is typical for real-world data. In order to avoid its negative impact on methods of time series analysis, noise reduction procedures may be used. The achieved results of an application of such procedures in identification of chaos or nonlinearity seem to be encouraging. One of the noise reduction methods is the Schreiber method, which, as it has been shown, is able to effectively reduce a noise added to time series generated by deterministic systems with chaotic dynamics. However, while analyzing real-world data, a researcher usually cannot be sure if the generating system is deterministic. Therefore, there is a risk that a noise reduction method will be applied to random data. In this paper, it has been shown that in situations where there in no clear evidence that investigated data are generated by a deterministic system, the Schreiber noise reduction method may negatively affect identification of time series. In the simulation carried out in this paper, the BDS test, the mutual information measure and the Pearson autocorrelation coefficient were used. The research has shown that an application of the Schreiber method may introduce spurious nonlinear dependencies to investigated data. As a result, random series may be misidentified as nonlinear.
PL
Jednym ze sposobów ograniczenia negatywnego wpływu obecności szumu losowego na analizę rzeczywistych szeregów czasowych jest stosowanie metod redukcji szumu. Prezentowane w literaturze przedmiotu rezultaty zastosowania takich procedur w procesie identyfikacji nieliniowości i chaosu są zachęcające. Jedną z metod redukcji szumu jest metoda Schreibera, która, jak wykazano, prowadzi do efektywnej redukcji szumu losowego dodanego do danych wygenerowanych z systemów deterministycznych o dynamice chaotycznej. Jednakże w przypadku danych rzeczywistych, badacz zwykle pozbawiony jest wiedzy, czy system generujący rzeczywiście jest deterministyczny. Istnieje więc ryzyko, że redukcji szumu zostaną wówczas poddane dane losowe. W niniejszym artykule wykazano, iż w sytuacji, gdy brak jest wyraźnych podstaw do stwierdzenia, że badany szereg pochodzi z systemu deterministycznego, metodę Schreibera należy stosować z dużą ostrożnością. Z przeprowadzonych symulacji, w których wykorzystano test BDS, miarę informacji wzajemnej oraz współczynnik korelacji liniowej Pearsona wynika bowiem, że redukcja szumu może wprowadzić do analizowanych danych, zależności o charakterze nieliniowym. W efekcie szeregi losowe mogą zostać błędnie zidentyfikowane jako nieliniowe.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.