- author = "Roisin Bernadette Loughran",
- title = "Music Instrument Identification with Feature Selection Using Evolutionary Methods",
- school = "University of Limerick",
- year = "2009",
- address = "Ireland",
- month = sep,
- keywords = "genetic algorithms, genetic programming, feature selection, ANN, PCA, tree visualisation, bloat",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/LoughranThesis.pdf",
- size = "281 pages",
-
abstract = "Musical instruments may be identified using machine
learning methods, but it is not clear which aspects of
the sound or features are best used in such methods.
Classification experiments using Principal Component
Analysis (PCA) and Multi-Layered Perceptrons (MLP) in
this thesis and that the addition of extra features may
not necessarily be beneficial - optimisation of the
features is required. This optimisation is implemented
using Evolutionary Computation methods as they have yet
to be extensively applied in musical sound analysis.
A Genetic Algorithm (GA) with a new instrument-clustering fitness function based on PCA is applied to optimise a set of 95 features for classification with an MLP. With this method, the number of features used to classify an instrument is reduced from 95 to as low as 22 with a classification accuracy reduction of less than 0.3percent. This method is tested against another evolutionary method that has not yet been applied to instrument identification - Genetic Programming (GP). GP is used to evolve a classifier program that can identify unseen samples with an accuracy of 94.3percent using just 14 of the 95 original features. Though not as high as the MLP or the GA-MLP, it is found that the GP is more consistent with its choice of features, offering a possible insight into timbre and the nature of sound recognition.
In both EC methods it is found that the first principal component of the envelope of the centroid, a new measure of this feature, is the most important among all 95 features. It is also seen that each classification method performs significantly better when tested with a general set of samples, than with a one-octave sample set common to each instrument. The classifiers are compared to a set of human listening tests on particularly troublesome samples. It is seen that although the GA and GP are accurate at identifying general unseen samples, the human ear performs significantly better than both methods at identifying these difficult samples.",
-
notes = "Supervisors Dr. Jacqueline Walker and Dr. Niall
Griffith
Matlab MIR toolbox GPLAB",
