Abstract
In this work we propose an immune-based approach for designing of fuzzy systems. From numerical data and with membership function previously defined, the immune algorithm evolves a population of fuzzy classification rules based on the clonal selection, hypermutation and immune network principles, Once AIS are able to find multiple good solutions of the problem, accurate and diverse fuzzy systems are built in a single run. Hence, we construct an ensemble of these classifier in order to achieve better results. An ensemble of classifiers consists of a set of individual classifiers whose outputs are combined when classifying novel patterns. The good performance of an ensemble is strongly dependent of individual accuracy and diversity of its components. We evaluate the proposed methodology through computational experiments on some datasets. The results demonstrate that the performance of the obtained fuzzy systems in isolation is very good. However when we combine these systems, a significant improvement is obtained in the correct c ass cat on rate, outperforming the single best classifier.

Abstract
Sound morphing figures prominently as one of the most interesting sound transformation techniques due to its enormous creative potential. Most authors pose the problem of morphing sounds using perceptual requirements, but hardly ever evaluate their results mainly because perceptual evaluations are cumbersome and costly, and there are no standard objective evaluation criteria established for sound morphing. In this work we propose a formal evaluation procedure for sound morphing algorithms following three criteria, namely correspondence, intermediateness, and smoothness. The adoption of the proposed evaluation framework will help formalize the results towards more perceptually relevant morphed sounds.

Abstract
There has been a great collective effort in the search for perceptually meaningful sound transformation techniques. The transformation of sounds matching target sound descriptors is a promising candidate because the descriptors are thought to capture timbral dimensions corresponding to relevant perceptual features. However, matching the descriptors alone is not enough because there are a large number of perceptually different sounds with the same values of descriptors. In this work, we use evolutionary computation to search for the spectral envelope variation that best matches the target spectral shape descriptors. The result is a more independent control of the descriptors while preserving the overall perceptual features. © July 2009- All copyright remains with the individual authors.

Abstract
The aim of sound morphing is to obtain a result that falls perceptually between two (or more) sounds. In order to do this, we should be able to morph perceptually relevant features of sounds instead of blindly interpolating the parameters of a model. In this work we present automatic timbral morphing techniques applied to musical instrument sounds using high-level descriptors as features. High-level descriptors are measures of the acoustic correlates of salient timbre dimensions derived from perceptual studies, so that matching the descriptors becomes the goal itself to render the results more perceptually meaningful.

Abstract
Part of the University of Campinas (UNICAMP, www.unicamp.br), the Interdisciplinary Nucleus of Sound Communication (NICS, www.nics.unicamp.br) was founded in 1983 by the composer Raul do Valle and collaborators. Since then, NICS has maintained a longstanding list of participations in the academic and artistic musical scenario, specially the ones concerning the application of mathematical models and computing technology in sound synthesis methods and electroacoustic music composition. Here we describe the major activities that NICS has been recently engaged with, specially in the areas of interactive performance, computer music, robotics, evolutionary computation applied to musical creation and, recently, physiologic signals acquisition for the retrieval of cognitive and emotive musical features. © July 2009- All copyright remains with the individual authors.

Abstract
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.