The phenomenon of creativity has received a growing amount of attention from scholars working across a range of disciplines. While this research has produced many important insights, it has also traditionally tended to explore creativity in terms of the reception of products or outcomes, conceiving of it as a cognitive process that is limited to the individual domain of the creative agent. More recently, however, researchers have begun to develop perspectives on creativity that highlight the patterns of adaptive embodied interaction that occur between multiple agents, as well as the broader sociomaterial milieu they are situated in. This has promoted new understandings of creativity, which is now often considered as a distributed phenomenon. Because music involves such a wide range of socio-cultural, bodily, technological, and temporal dimensions it is increasingly taken as a paradigmatic example for researchers who wish to explore creativity from this more relational perspective. In this article, we aim to contribute to this project by discussing musical creativity in light of recent developments in embodied cognitive science. More specifically, we will attempt to frame an approach to musical creativity based in an 4E (embodied, embedded, enactive, and extended) understanding of cognition. We suggest that this approach may help us better understand creativity in terms of how interacting individuals and social groups bring forth worlds of meaning through shared, embodied processes of dynamic interactivity. We also explore how dynamical systems theory (DST) may offer useful tools for research and theory that align closely with the 4E perspective. To conclude, we summarize our discussion and suggest possibilities for future research.
Fostered by the introduction of the Music Information Retrieval Evaluation eXchange (MIREX) competition, the number of systems which calculate Symbolic Melodic Similarity has recently increased considerably. In order to understand the state of the art, we provide a comparative analysis of existing algorithms. The analysis is based on eight criteria that help characterising the systems, and highlighting strengths and weaknesses. We also propose a taxonomy which classifies algorithms based on their approach. Both taxonomy and criteria are fruitfully exploited for providing input for new forthcoming research in the area. «Start article»The advent of the Internet has made a large quantity of audio and symbolic musical data freely available. The analysis of these data can provide useful insights into several aspects of music. By comparing many musical pieces, it is possible to abstract relevant rules and processes which characterise a particular style. Also, the analysis of large databases can improve our understanding of the generative process, shedding light on the evolutionary path undergone by music over time. In order to capitalise upon the significant body of knowledge currently stored within online music datasets, a number of reliable and efficient automatic tools have been developed over the last decades.Melodic similarity-detection algorithms are an instance of such tools. When used on 1 online musical datasets, they can provide valuable information on intra-and inter-work melodic relationships and on the underlying melodic structures of the pieces analysed.Given two or more sequences of notes, Symbolic Melodic Similarity aims to evaluate their degree of likeness, as human listeners are able to do. This task has relevance both within the academy and in industry. For instance, beyond the purely academic benefits of identifying the degree of likeness between musical pieces and composer-practices afforded by melodic similarity systems, plagiarism detection constitutes an example of a practical application of this task with clear legal and commercial implications. Many algorithms for judging melodic similarity have been introduced over the years. Even though such tools perform essentially the same task, they may be based on theories and methods which belong to radically different disciplines. For example, there are some algorithms based on principles from music theory, others based on cognitive constraints, and others which implement notions from pure mathematics. Hofmann-Engl 2010). This evident lack of evaluation of state-of-the-art techniques is the first motivation for this paper. Accessibility is a second motivation for the paper. The literature on Symbolic Melodic Similarity is distributed across many different sources, which cover numerous topics from computer science to music theory. This survey brings together recent studies on Symbolic Melodic Similarity, describing them in a concise way, so that researchers can form an initial overview of the approaches used by other scholars The paper propose...
The article introduces recursive ontology, a general ontology which aims to describe how being is organized and what are the processes that drive it. In order to answer those questions, I use a multidisciplinary approach that combines the theory of levels, philosophy and systems theory. The main claim of recursive ontology is that being is the product of a single recursive process of generation that builds up all of reality in a hierarchical fashion from fundamental physical particles to human societies. To support this assumption, I provide the general laws and the basic principles of recursive ontology as well as a semi-formalised model of the theory based on a recursive generative grammar. Recursive ontology not only actively promotes a multidisciplinary investigation of reality, but also can be used as a general framework to develop future domain-specific theories.
Abstract. In the field of computational creativity, the area of automatic music generation deals with techniques that are able to automatically compose human-enjoyable music. Although investigations in the area started recently, numerous techniques based on artificial intelligence have been proposed. Some of them produce pleasant results, but none is able to effectively evolve the style of the musical pieces generated. In this paper, we fill this gap by proposing an evolutionary memetic system that composes melodies, exploiting a society of virtual composers. An extensive validation, performed by using both quantitative and qualitative analyses, confirms that the system is able to evolve its compositional style over time.
Events Study Day on Computer Simulation of Musical Creativity University of Huddersfield, United Kingdom, 27 June 2015. Information about the study day and video recordings of the presentations given during the event can be found at https://simulationofmusicalcreativity .wordpress.com/.
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