@inproceedings{0c02d386341f433b8d566a430098f8f1,
title = "Genetic programming for musical sound analysis",
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 94%. 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 94.3% and the average accuracy from 68.2% to 75.67%. 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.",
keywords = "Genetic Programming, Musical Information Retrieval, timbre",
author = "R{\'o}is{\'i}n Loughran and Jacqueline Walker and Michael O'Neill and James McDermott",
year = "2012",
doi = "10.1007/978-3-642-29142-5_16",
language = "English",
isbn = "9783642291418",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "176--186",
booktitle = "Evolutionary and Biologically Inspired Music, Sound, Art and Design - First International Conference, EvoMUSART 2012, Proceedings",
note = "1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012 ; Conference date: 11-04-2012 Through 13-04-2012",
}