@conference {17,
	title = {Musical Composer Identification through Probabilistic and Feedforward Neural Networks},
	booktitle = {Applications of Evolutionary Computation},
	year = {2010},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	abstract = {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{\textquoteright}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.},
	doi = {10.1007/978-3-642-12242-2\_42},
	author = {M. A. Kaliakatsos-Papakostas and M. G. Epitropakis and M. N. Vrahatis},
	editor = {Di Chio, Cecilia and Brabazon, Anthony and Di Caro, Gianni and Ebner, Marc and Farooq, Muddassar and Fink, Andreas and Grahl, J{\"o}rn and Greenfield, Gary and Machado, Penousal and O{\textquoteright}Neill, Michael and Tarantino, Ernesto and Urquhart, Neil}
}
