Music Instrument Identification with Feature Selection Using Evolutionary Methods

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  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 =          "",
  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

                 Matlab MIR toolbox GPLAB",

Genetic Programming entries for Roisin Loughran