Paper Info
Title
Musical composer identification through probabilistic and feedforward neural networks
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'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.
Year
DOI
Venue
2010
10.1007/978-3-642-12242-2_42
EvoApplications (2)
Keywords
Field
DocType
probabilistic neural networks,feedforward neural network,music information retrieval,different composer,information capacity,classical gradient-based method,computational intelligence method,classical composer,musical composer identification,trained feedforward neural networks,composer classification,dodecaphonic trace vector,utility computing,differential evolution,probabilistic neural network,experimental analysis
Music information retrieval,Feedforward neural network,Computational intelligence,Computer science,Differential evolution,Probabilistic neural network,Artificial intelligence,Probabilistic logic,Artificial neural network,Strengths and weaknesses,Machine learning
Conference
Volume
ISSN
ISBN
6025
0302-9743
3-642-12241-8
Citations 
PageRank 
References 
4
0.51
16
Authors
3
Name
Order
Citations
PageRank
Maximos A. Kaliakatsos-Papakostas16713.26
Michael G. Epitropakis2938.39
M.N. Vrahatis31740151.65