M. F. Caetano, J. Manzolli, and F. J. Von Zuben, University of Campinas
In this paper, we present a sound synthesis method that utilizes evolution as generative paradigm. Such sounds will be thereon referred to as evolutionary sounds. Upon defining a population of complex sounds, i.e. sound segments sampled from acoustical instruments and speech; we generated sounds that resulted from evolution applied to those populations. The methodology presented here is an extension to the Evolutionary Sound Synthesis Method (ESSynth) created recently. In ESSynth, a set of waveforms, the Population, is evolved towards another set, the Target, through the application of a Genetic Algorithm (GA). Fitness evaluation is a mathematical distance metric. We enhance features of the previous implementation herein and present the codification. The genetic operators and selection criterion applied are depicted together with the relevant genetic parameters involved in the process. To evaluate the results we present a sound taxonomy based on an objective and a subjective criterion. Those criteria are discussed, the experimental procedure is explained and the results are depicted and evaluated.