José Fornari

The evolutionary sound synthesis method

A mathematical model for interactive sound synthesis based on the application of Genetic Algorithms (GA) is presented. The Evolutionary Sound Synthesis Method (ESSynth) generates sequences of waveform variants by the application of genetic operators on an initial population of waveforms. We describe how the waveforms can be treated as genetic code, the fitness evaluation methodology and how genetic operations such as crossover and mutation are used to produce generations of waveforms. Finally, we discuss the results evaluating the generated sounds.

Creating Soundscapes Using Evolutionary Spatial Control

A new way to control sound spatial dispersion using the ESSynth Method is introduced here. The Interaural Time Difference (ITD) is used as genotype of an evolutionary control of sound spatialization. Sound intensity and the ITD azimuth angle are used to define spatial dispersion and spatial similarity. Experimental results where crossover and mutation rates were used to create spatial sonic trajectories are discussed.

Abstrações: An audiovisual installation based on motion recognizing

This paper aims to present an overview on Abstrações, an audiovisual installation controlled by video motion parameters. This article focuses on an implemented state machine model, the interaction between video motion recognition, the audio and video processing and the states of its implementation.

Abduction and Meaning in Evolutionary Soundscapes

The creation of an artwork named RePartitura is discussed here under principles of Evolutionary Computation (EC) and the triadic model of thought: Abduction, Induction and Deduction, as conceived by Charles S. Peirce. RePartitura uses a custom-designed algorithm to map image features from a collection of drawings and an Evolutionary Sound Synthesis (ESSynth) computational model that dynamically creates sound objects. The output of this process is an immersive computer generated sonic landscape, i.e. a synthesized Soundscape. The computer generative paradigm used here comes from the EC methodology where the drawings are interpreted as a population of individuals as they all have in common the characteristic of being similar but never identical. The set of specific features of each drawing is named as genotype. Interaction between different genotypes and sound features produces a population of evolving sounds. The evolutionary behavior of this sonic process entails the self-organization of a Soundscape, made of a population of complex, never-repeating sound objects, in constant transformation, but always maintaining an overall perceptual self-similarity in order to keep its cognitive identity that can be recognize for any listener. In this article we present this generative and evolutionary system and describe the topics that permeates from its conceptual creation to its computational implementation. We underline the concept of self-organization in the generation of soundscapes and its relationship with computer evolutionary creation, abductive reasoning and musical meaning for the computational modeling of synthesized soundscapes.

An Essynth implementation in PD

A great variety of audio synthesis methods have been developed since the beginning of electronic technology era. These methods can be organized in three categories: 1) linear methods, such as the additive and subtractive synthesis, 2) non-linear methods, such as FM and stochastic synthesis, and 3) editing methods, such wavetable and granular synthesis. However, all these categories have something in common; they are deterministic methods, once that all present a static output (synthesized audio) when the controlling parameters are fixed. In constrast, Essynth is a non-deterministic method of audio synthesis once that its output thrives to adapt to certain conditions, even if the controlling parameters remains unchanged.

A computational environment for the evolutionary sound synthesis of birdsongs

Birdsongs are an integral part of many natural environments. They constitute an ecological network of sonic agents whose interaction is self-organized into an open complex system of similar cognitive characteristics, at the same time that it continuously generates original acoustic data. This work presents a preliminary study on the development of an evolutionary algorithm for the generation of an artificial soundscape of birdsongs. This computational environment is created by genetic operators that dynamically generate sequences of control parameters for computational models of birdsongs, given by the physical model of a syrinx. This system is capable of emulating a wide range of realistic birdsongs and generating with them a network of bird calls. The result here presented is an artificial evolutionary soundscape that is also interactive, as it can receive external data, such as from instant text messages like the ones from the micro-blog Twitter, and map them as the genotype of new individuals belonging to a dynamic population of artificial birdsongs.

Path of patches: implementing an evolutionary soundscape art installation

Computational adaptive methods have already been used in Installation art. Among them, Generative artworks are those that value the artistic process, rather than its final product, which can now be of multimodal nature. Evolutionary Algorithms (EA) can be successfully applied to create a Generative art process that is self-similar yet always new. EAs allow the creation of dynamic complex systems from which identity can emerge. In computational sonic arts, this is ecological modeling; here named Evolutionary Soundscape. This article describes the development and application of an EA computing system developed to generate Evolutionary Soundscapes in a Multimodal Art Installation. Its physical structure uses paths of forking pipes attached to fans and microphones that capture audio to feed the EA system that creates the Soundscape. Here is described the EA system; its design in PureData (PD); its connection with the physical structure; its artistic endeavor and final sonic accomplishments.