Genetic programming for musical sound analysis

Authors:
Róisín Loughran;Jacqueline Walker;Michael O'Neill;James McDermott
Affiliations:
University of Limerick, Limerick, Ireland;University of Limerick, Limerick, Ireland;NCRA, University College Dublin, Ireland;NCRA, University College Dublin, Ireland
Venue:
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Year:
2012

Citing 2
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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.