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Evolutionary Soud Synthesis Controlled by Gestural Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . the whole collection. Given that it is impossible to draw by hand two identical sketches, it follows that there would never be two identical mappings, which implies a Population set without clones; although these individuals are strongly related. In contrast to that, dance body actions did not aim for similarity. To deal with such context, Rudolf Laban developed a system to represent distinct movements of dance. Movements, supposed to be unique that were defined as dimensions, would represent any dance movement, somehow working like the orthogonal coordinates of the Cartesian space. Here, these dimensions were mapped into sonic genotypes of the Individuals Population set (see Table 1). That is why the Individuals whose Genotypes came from Dance Gestures were more heterogeneous than the Individuals whose Genotype came from Drawing Gestures. This is observed in Figure 5, which presents two sonograms taken from audio excerpts, recorded from the sound output created by these two types of gestural data. It is possible to observe that the left sonogram generated by dance movements produce Tonal sounds, in contrast with the right sonogram, were the gestural drawings engender Stochastic sounds. These examples can be heard at the following link: http://www.4shared.com/dir/GXH0h6_G/JNMR2011.html. 148 Fig. 5: Sonogram (top) and waveform (bottom) of two audio recordings from sound outputs generated by dance movements (left) and drawing mappings (right). It is interesting to observe in Figure 5 that the audio excerpt generated by the ESSynth fed with gestural data retrieved from dance movements presents more variation of intensity (dynamics) than the one generated by the drawings, which almost seems as having been compressed. Dance movements produced a sound texture with cumulative frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . opus
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FORNARI; SHELLARD; MANZOLLI concentration, while the drawings created dense and spread sonograms. For each drawing image, the mapping algorithm automatically found about thirty different graphic objects: one Cumulation and many Repetitions and Fragments. The mapping process took all of them into consideration, to create the Genotype. This seems to have enlarged the sonogram’s density, as seen in the sonogram at the right side of Figure 5. Dance gestural data, on the other hand, was constituted by 24 time-series, generated by 4 movement sensors (accelerometers). They described near-to-periodic movements, which resemble sinusoidal waveforms. Thus, the movements tended to be acoustically represented as quasi-periodic sounds. That corresponds sound objects more deterministic (with pitch) than stochastic (without pitch). This is observed in the sonogram at the left side of Figure 5. When comparing these two distinct results, the drawings and the dancer's movements emerged as two distinct sonorities, which can be seen as different CAS attractors (HOLLAND, 1992). These two aspects show different forms of representing adaptive gestural processes, in the Evolutionary Sound Synthesis context. Therefore, these two types of sound objects converged to sound realms with distinct sonic features. According to Varèse's viewpoint, the concept of organized sound is about arranging together timbre and rhythm in musical structures (BERNARD, 1987). We understand the self-organized sonic results of our system as transcending Varèse's concept, where those arrangements are here extended by the emergent behavior of attractors. Conclusions A new generation of artists has applied Evolutionary Computation to emulate creative processes in computers (BENTLEY, 1999). The Evolutionary Sound Synthesis method presented here can be seen as a process of this sort. It integrates a Processual Artwork and a real-time Computer Music system, by means of Gestural Data. This method can be used to indefinitely extend, in the sound domain, the process of artworks already finished, such as a collection of drawing or records of dance movements. This is especially useful for Multimedia Art Installations. The gestures of the artist, imprinted in a physical medium (such as drawings on paper), can now be retrieved as Genotypes of a Population of Individuals, altogether forming a new, never-repeating, never-ending evolutionary sonic process, that converges to a self-similarity, thus providing reminiscent traces of the previous processual artwork. This is like bringing back to life, in sonic media, a processual artwork already over. This current implementation does not have Target set, or Selection process. However, it does have a variable-size Population set. Here, the evolution process is adrift, opus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
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OPUS REVISTA DA ANPPOM ASSOCIAÇÃO
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sumário volume 17 • número 1
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instruções para autores Criada em
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