Abstract. In this paper we report on the synthesis of sounds using cellular automata, specifically the multitype voter model. The mapping process adopted is based on digital signal processing analysis of automata evolutions and consists in mapping histograms onto spectrograms. The main problem of cellular automata is the difficulty of control and, consequently, sound synthesis methods based on these computational models normally present a high factor of randomness in the output. We have achieved a significant degree of control as to predict the type of sounds that we can obtain. We are able to develop a flexible sound design process with emphasis on the possibility of controlling over time the spectrum complexity.
This article presents a free framework for collaborative creation of interactive and experimental computer music called Soundcool. It is designed to fill a gap between rigid ready-to-use applications and flexible programming languages. The system offers easy-to-use elements for generating and processing sound, much like ready-made applications, but it enables flexible configuration and control, more like programming languages. The system runs on personal computers with an option for control via smartphones, tablets, and other devices using the Open Sound Control (OSC) protocol. Originally developed to support a new music curriculum, Soundcool is being used at different educational institutions in Spain, Portugal, Italy, and Romania through EU-funded Erasmus+ projects. In this article, we present our system and showcase three different scenarios as examples of how our system meets its objectives as an easy-to-use, versatile, and creative tool.
This paper addresses four criteria that the Soundcool project meets: to "be sustainable", "be future-oriented", "be transformative" and "be innovative". Soundcool is a pedagogical and technological project. A brief description of the technology behind Soundcool will be useful for the reader before addressing the four criteria. Soundcool is like a "Lego" for sound; Soundcool is composed of a series of software modules that run on a central computer, or host computer. Each module is sort of a musical instrument; it could be a synthesizer, a sampler, a sound effect processor, etc. these modules can be interconnected in different ways allowing the users, i.e. the students, to create their own arrangements, as we call the module creations and interconnections. Then, each module can be controlled either with the mouse or, what is more interesting, with a mobile device through WiFi. This way, every student can control one or several modules of the whole arrangement from their mobile device contributing to a collaborative and participative experience.
Abstract:In this paper we introduce a new approach to algorithmic sound composition using a bespoke technique combining coupled Cellular Automata (CA) and Histogram Mapping Synthesis. Two CA are used: a hodge podge machine and a growth model. The latter serves as control of the former. The hodge podge machine can exhibit different kinds of behaviour depending on the values of a set of rule parameters. Our method explores the fact that different simultaneous behaviours can be evolved within the same automaton if we bring into play different sets of parameter values. However, we restrict the number of parameter sets to two. Therefore, the CA growth model will have only two states and will delimit two dynamic zones in the hodge podge machine, each of which governed by a different set of parameter values. The predictable evolution of the two zones will produce a controlled dynamic sound spectrum. Among all the possibilities that this process affords for the composition of a variety of sounds algorithmically, we highlight its application to the attack portion of a sound, making it dynamically more complex than the rest of the sound.
Histogram mapping synthesis (HMS) is a new technique for sound design based on cellular automata (CA). Cellular automata are computational models that create moving images. In the context of HMS, and based on a novel digital signal processing approach, these images are analyzed by histogram measurements, giving a sequence of histograms as a result. In a nutshell, these histogram sequences are converted into spectrograms that, in turn, are rendered into sounds. Unlike other CA-based systems, the HMS mapping process is not intuition-based, nor is it totally arbitrary; it is based instead on resemblances discovered between the components of the histogram sequences and the spectral components of the sounds. Our main concern is to address the problem of the sound-design limitations of synthesis techniques based on CA. These limitations stem, fundamentally, from the unpredictable and autonomous nature of these computational models. As a result, one of the main advantages of HMS is that it affords more control over the sound-design process than other sound-synthesis techniques using CA. The timbres that we have designed with HMS range from those that are novel to those that are imitations of sounds produced by acoustic means. All the sounds obtained present dynamic features, and many of them, including some of those that are novel, retain important characteristics of sounds produced by acoustic means.
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