<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. A. Kaliakatsos-Papakostas</style></author><author><style face="normal" font="default" size="100%">M. G. Epitropakis</style></author><author><style face="normal" font="default" size="100%">A. Floros</style></author><author><style face="normal" font="default" size="100%">M. N. Vrahatis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Chaos and Music: From time series analysis to evolutionary composition</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Bifurcation and Chaos (IJBC)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.worldscientific.com/doi/abs/10.1142/S0218127413501812</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">1350181</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Music is an amalgam of logic and emotion, order and dissonance, along with many combinations of contradicting notions which allude to deterministic chaos. Therefore, it comes as no surprise that several research works have examined the utilization of dynamical systems for symbolic music composition. The main motivation of the paper at hand is the analysis of the tonal composition potentialities of several discrete dynamical systems, in comparison to genuine human compositions. Therefore, a set of human musical compositions is utilized to provide ``compositional guidelines'' to several dynamical systems, the parameters of which are properly adjusted through evolutionary computation. This procedure exposes the extent to which a system is capable of composing tonal sequences that resemble human composition. In parallel, a time series analysis on the genuine compositions is performed, which firstly provides an overview of their dynamical characteristics and secondly, allows a comparative analysis with the dynamics of the artificial compositions. The results expose the tonal composition capabilities of the examined iterative maps, providing specific references to the tonal characteristics that they can capture.</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. A. Kaliakatsos-Papakostas</style></author><author><style face="normal" font="default" size="100%">M. G. Epitropakis</style></author><author><style face="normal" font="default" size="100%">M. N. Vrahatis</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Agon, Carlos</style></author><author><style face="normal" font="default" size="100%">Andreatta, Moreno</style></author><author><style face="normal" font="default" size="100%">Assayag, Gérard</style></author><author><style face="normal" font="default" size="100%">Amiot, Emmanuel</style></author><author><style face="normal" font="default" size="100%">Bresson, Jean</style></author><author><style face="normal" font="default" size="100%">Mandereau, John</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Feature Extraction Using Pitch Class Profile Information Entropy</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematics and Computation in Music</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-21590-2_32</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin / Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">6726</style></volume><pages><style face="normal" font="default" size="100%">354-357</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Computer aided musical analysis has led a research stream to explore the description of an entire musical piece by a single value. Combinations of such values, often called global features, have been used for several identification tasks on pieces with symbolic music representation. In this work we extend some ideas that estimate information entropy of sections of musical pieces, to utilize the Pitch Class Profile information entropy for global feature extraction. Two approaches are proposed and tested, the first approach considers musical sections as overlapping sliding onset windows, while the second one as non-overlapping fixed-length time windows.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. A. Kaliakatsos-Papakostas</style></author><author><style face="normal" font="default" size="100%">M. G. Epitropakis</style></author><author><style face="normal" font="default" size="100%">M. N. Vrahatis</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Di Chio, Cecilia</style></author><author><style face="normal" font="default" size="100%">Brabazon, Anthony</style></author><author><style face="normal" font="default" size="100%">Di Caro, Gianni</style></author><author><style face="normal" font="default" size="100%">Drechsler, Rolf</style></author><author><style face="normal" font="default" size="100%">Farooq, Muddassar</style></author><author><style face="normal" font="default" size="100%">Grahl, Jörn</style></author><author><style face="normal" font="default" size="100%">Greenfield, Gary</style></author><author><style face="normal" font="default" size="100%">Prins, Christian</style></author><author><style face="normal" font="default" size="100%">Romero, Juan</style></author><author><style face="normal" font="default" size="100%">Squillero, Giovanni</style></author><author><style face="normal" font="default" size="100%">Tarantino, Ernesto</style></author><author><style face="normal" font="default" size="100%">Tettamanzi, Andrea</style></author><author><style face="normal" font="default" size="100%">Urquhart, Neil</style></author><author><style face="normal" font="default" size="100%">Uyar, A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Weighted Markov Chain Model for Musical Composer Identification</style></title><secondary-title><style face="normal" font="default" size="100%">Applications of Evolutionary Computation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Berlin / Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Several approaches based on the ‘Markov chain model’ have been proposed to tackle the composer identification task. In the paper at hand, we propose to capture phrasing structural information from inter onset and pitch intervals of pairs of consecutive notes in a musical piece, by incorporating this information into a weighted variation of a first order Markov chain model. Additionally, we propose an evolutionary procedure that automatically tunes the introduced weights and exploits the full potential of the proposed model for tackling the composer identification task between two composers. Initial experimental results on string quartets of Haydn, Mozart and Beethoven suggest that the proposed model performs well and can provide insights on the inter onset and pitch intervals on the considered musical collection.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. A. Kaliakatsos-Papakostas</style></author><author><style face="normal" font="default" size="100%">M. G. Epitropakis</style></author><author><style face="normal" font="default" size="100%">M. N. Vrahatis</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Di Chio, Cecilia</style></author><author><style face="normal" font="default" size="100%">Brabazon, Anthony</style></author><author><style face="normal" font="default" size="100%">Di Caro, Gianni</style></author><author><style face="normal" font="default" size="100%">Ebner, Marc</style></author><author><style face="normal" font="default" size="100%">Farooq, Muddassar</style></author><author><style face="normal" font="default" size="100%">Fink, Andreas</style></author><author><style face="normal" font="default" size="100%">Grahl, Jörn</style></author><author><style face="normal" font="default" size="100%">Greenfield, Gary</style></author><author><style face="normal" font="default" size="100%">Machado, Penousal</style></author><author><style face="normal" font="default" size="100%">O’Neill, Michael</style></author><author><style face="normal" font="default" size="100%">Tarantino, Ernesto</style></author><author><style face="normal" font="default" size="100%">Urquhart, Neil</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Musical Composer Identification through Probabilistic and Feedforward Neural Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Applications of Evolutionary Computation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Berlin / Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">During the last decade many efforts for music information retrieval have been made utilizing Computational Intelligence methods. Here, we examine the information capacity of the Dodecaphonic Trace Vector for composer classification and identification. To this end, we utilize Probabilistic Neural Networks for the construction of a similarity matrix of different composers and analyze the Dodecaphonic Trace Vector’s ability to identify a composer through trained Feedforward Neural Networks. The training procedure is based on classical gradient-based methods as well as on the Differential Evolution algorithm. An experimental analysis on the pieces of seven classical composers is presented to gain insight about the most important strengths and weaknesses of the aforementioned approach.</style></abstract></record></records></xml>