Automatic tuning of the OP-1 synthesizer using a multi-objective genetic algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

@MastersThesis{Macret2013aa,
  author =       "Matthieu Michel Jean Macret",
  title =        "Automatic tuning of the {OP-1} synthesizer using a
                 multi-objective genetic algorithm",
  school =       "Communication, Art \& Technology: School of
                 Interactive Arts and Technology, Simon Fraser
                 University",
  year =         "2013",
  address =      "Vancouver, Canada",
  month =        jul # ", 16",
  keywords =     "Genetic Algorithms, genetic programming, DEAP,
                 Artificial Intelligence, Sound Synthesis,
                 Multi-objective Optimization",
  date-added =   "2018-07-31 16:52:16 +0900",
  date-modified = "2018-07-31 16:53:11 +0900",
  URL =          "http://summit.sfu.ca/item/13452",
  size =         "108 pages",
  abstract =     "Calibrating a sound synthesizer to replicate or
                 approximate a given target sound is a complex and time
                 consuming task for musicians and sound designers. In
                 the case of the OP1, a commercial synthesizer developed
                 by Teenage Engineering, the difficulty is multiple. The
                 OP-1 contains several synthesis engines, effects and
                 low frequency oscillators, which make the parameters
                 search space very large and discontinuous. Furthermore,
                 interactions between parameters are common and the OP-1
                 is not fully deterministic. We address the problem of
                 automatically calibrating the parameters of the OP-1 to
                 approximate a given target sound. We propose and
                 evaluate a solution to this problem using a
                 multi-objective
                 Non-dominated-Sorting-Genetic-Algorithm-II. We show
                 that our approach makes it possible to handle the
                 problem complexity, and returns a small set of presets
                 that best approximate the target sound while covering
                 the Pareto front of this multi-objective optimization
                 problem.",
  notes =        "Some comparison with GP",
}

Genetic Programming entries for Matthieu Macret

Citations