16,901 research outputs found
What makes a musical improvisation creative?
No full textBackground in musical improvisation and creativity. What makes musical improvisation
creative? And what exactly is it that justifies one improviser being described as more creative
than another? For a clearer understanding, it is a practical necessity to use an approach such as
those of Berliner (1994) and Gibbs (2010), who make the study of improvisational creativity
more tangible by identifying key constituent parts, rather than treat creativity as ineffable
(Bailey 1993).
Background in computational linguistics. The log likelihood ratio statistic can be used to
compare two sets of texts (corpora) to examine word distribution patterns (Rayson & Garside
2000, Dunning 1993). Using this statistic, words are identified which are associated with
academic papers on creativity. Lin’s similarity measure (Lin 1998) is then used as a basis for
clustering words with similar meanings using the algorithm Chinese Whispers (Biemann 2006).
Analysis of the clusters reveals fourteen key components of creativity.
Aims. To model creativity in musical improvisation by identifying components of creativity
using computational linguistics techniques and understanding how each contributes to
creativity in improvisation.
Main contribution. The paper presents an empirical, language-based approach to
understanding creativity in musical improvisation. This approach is based upon treating
creativity as having common features that transcend different types of creativity but that vary in
importance depending on the type of creativity. Fourteen key components of creativity are
identified from an analysis of a corpus of texts on creativity. A study is then conducted to
investigate the relative importance of each of these components in musical improvisational. All
fourteen components are considered relevant to some degree, but particular significance is
attached to three of them: the ability to communicate and interact, the possession of relevant
musical knowledge and skills, and emotional engagement and intention. It is notable that the
products of improvisation are relatively less important than these process-based aspects.
Implications. The work provides a model of musical improvisational creativity as a set of
guidelines or benchmarks for evaluating how creative a musical improviser is. Such a detailed
understanding helps improvisers identify what areas to work on in order to develop their
creativity (Gibbs 2010)
Score Following: An artificially intelligent musical accompanist (Jordanous 2007, Jordanous & Smaill 2008+2009) - code
No full textMax/MSP code, documentation and technical reports (papers) for the HMM score follower reported in Jordanous (2007) MSc thesis, 2008 CIM paper and 2009 JNMR paper. The score follower uses hidden markov models to provide live automatic accompaniment (computer-generated) in live performance by a soloist, matching the soloist's tempo and expression, and following their position in the score
Waiting... with Rachel and Peter: Podcast funded by Arts Council England/Wellcome Trust
No full textFuel, Roundhouse and King’s Cultural Institute present
Waiting… with Rachel and Peter
By Stefan Kaegi in collaboration with Anna Jordanous and Niki Neecke. Voices by Acapela Group.
Waiting… with Rachel and Peter is the fourth in our new series of podcasts called While You Wait, each of which is a different meditation on the idea of waiting and created by artists in collaboration with academics from King's College London.
Waiting… with Rachel and Peter has been made by Berlin based artist Stefan Kaegi in collaboration with Anna Jordanous, Research Associate, Centre for e-Research and sound designer Niki Neecke.
While You Wait is funded by Arts Council England and a Wellcome Trust Arts Award.
An accompanying video interview featuring Stefan Kaegi and Anna Jordanous is available at https://www.youtube.com/watch?v=GcG43H_jMN
An Evaluation of the Impact of Constraints on the Perceived Creativity of Narrative Generating Software
No full textThis work investigates the impact of constraints on the
perceived creativity of the output of narrative generating
systems, in order to understand what level of constraint
application results in the most creative output.
To achieve this, software is written that generates short
stories, using adjustable levels of constraint meant to
reflect those utilised by other narrative generating systems.
These systems are presented at different positions
along a spectrum, which we posit arises from the application
of constraint. The creativity of the output is then
assessed by human evaluators. The results are promising
and show a clear variation of response based on the
level of constraint imposed on the narrative generation
process. The results show a sweet spot for maximal creativity
closer to the less constrained end of the spectrum,
which demonstrates the potential for more creative software
by the relaxing of constraints
Report on the Eighth International Conference on Computational Creativity
No full textThe Eighth International Conference on Computational Creativity (ICCC’17)1 was hosted at the Georgia Institute of Technology in Atlanta, Georgia, USA from June 19th - June 23rd, 2017. The ICCC’17 organising committee consisted of Ashok Goel (General Chair), Kazjon Grace (Workshop Co-chair), Matthew Guzdial (Media Chair), Mikhail Jacob (Local Chair), Anna Jordanous (Program Co-chair), Ruli Manurung (Workshop Co-chair) and Alison Pease (Program Co-chair). This report summarises the main topics addressed
Four PPPPerspectives on Computational Creativity
No full textFrom what perspective should creativity of a system be considered? Are we interested in the creativity of the system’s out- put? The creativity of the system itself? Or of its creative processes? Creativity as measured by internal features or by external feedback? Traditionally within computational creativity the focus had been on the creativity of the system’s Products or of its Processes, though this focus has widened recently regarding the role of the audience or the field surrounding the creative system. In the wider creativity research community a broader take is prevalent: the creative Person is considered as well as the environment or Press within which the creative entity operates in. Here we have the Four Ps of creativity: Person, Product, Process and Press. This paper presents the Four Ps, explaining each of the Four Ps in the context of creativity research and how it relates to computational creativity. To illustrate how useful the Four Ps can be in taking a fuller perspective on creativity, the concepts of novelty and value explored from each of the Four P perspectives, uncovering aspects that may otherwise be overlooked. This paper argues that the broader view of creativity afforded by the Four Ps is vital in guiding us towards more encompassing and comprehensive computational investigations of creativity
Evaluating evaluation: Assessing progress in computational creativity research
No full textComputational creativity research has produced many computational systems that are described as creative. A comprehensive literature survey reveals that although such systems are labelled as creative, there is a distinct lack of evaluation of the creativity of creative systems. As a research community, we should adopt a more scientific approach to evaluation of the creativity of our systems if we are to progress in understanding creativity and modelling it computationally. A methodology for creativity evaluation should accommodate different manifestations of creativity but also require a clear, definitive statement of the standards used for evaluation. This paper proposes Evaluation Guidelines, a standard but flexible approach to evaluation of the creativity of computational systems and argues that this approach should be taken up as standard practice in computational creativity research. The approach is outlined and discussed, then illustrated through a comparative evaluation of the creativity of jazz improvisation systems.
The longer term value of creativity judgements in computational creativity
No full textDuring research to develop the Standardised Procedure for Evaluating Creative Systems (SPECS) methodology for evaluat- ing the creativity of ‘creative’ systems, in 2011 an evaluation case study was carried out. The case study investigated how we can make a ‘snapshot’ decision, in a short space of time, on the creativity of systems in various domains. The systems to be evaluated were presented at the International Computational Creativity Conference in 2011. Evaluation was performed by people whose domain expertise ranges from expert to novice, depending on the system. The SPECS methodology was used for evaluation, and was compared to two other creativity evaluation methods (Ritchie’s criteria and Colton’s Creative Tripod) and to results from surveying people’s opinion on the creativity of the systems under investigation. Here, we revisit those results, considering them in the context of what these systems have contributed to computational creativity development. Five years on, we now have data on how influential these systems were within computational creativity, and to what extent the work in these systems has influenced further developments in computational creativity research. This paper investigates whether the evaluations of creativity of these systems have been helpful in predicting which systems will be more influential in computational creativity (as measured by paper citations and further development within later computational systems). While a direct correlation between evaluative results and longer term impact is not discovered (and perhaps too simplistic an aim, given the factors at play in determining research impact), some interesting alignments are noted between the 2011 results and the impact of papers five years on
Preface: Proceedings of the Eighth International Conference on Computational Creativity, ICCC 2017, Atlanta — 19 - 23 June
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