Genetic Programming for Musical Sound Analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Loughran:2012:EvoMUSART,

• author = "Roisin Loughran and Jacqueline Walker and Michael O'Neill and James McDermott",
• title = "Genetic Programming for Musical Sound Analysis",
• booktitle = "Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012",
• year = "2012",
• month = "11-13 " # apr,
• editor = "Penousal Machado and Juan Romero and Adrian Carballal",
• series = "LNCS",
• volume = "7247",
• publisher = "Springer Verlag",
• address = "Malaga, Spain",
• pages = "176--186",
• organisation = "EvoStar",
• isbn13 = "978-3-642-29141-8",
• DOI = "doi:10.1007/978-3-642-29142-5_16",
• keywords = "genetic algorithms, genetic programming, Musical Information Retrieval, timbre",
• abstract = "This study uses Genetic Programming (GP) in developing a classifier to distinguish between five musical instruments. Using only simple arithmetic and Boolean operators with 95 features as terminals, a program is developed that can classify 300 unseen samples with an accuracy of 94percent. The experiment is then run again using only 14 of the most often chosen features. Limiting the features in this way raised the best classification to 94.3percent and the average accuracy from 68.2percent to 75.67percent. This demonstrates that not only can GP be used to create a classifier but it can be used to determine the best features to choose for accurate musical instrument classification, giving an insight into timbre.",
• notes = "Part of \cite{Machado:2012:EvoMusArt_proc} EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012",

}

Genetic Programming entries for Roisin Loughran Jacqueline Walker Michael O'Neill James McDermott

Citations