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annajordanous

Discovered public repositories for annajordanous in the GitHub catalog.

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annajordanous/sharingancientwisdoms

Code, stylesheets and data generated for the Sharing Ancient Wisdoms project ( http://www.ancientwisdoms.ac.uk ) using TEI/XML, RDF and a custom ontology based on CIDOC-CRM to model information about relations between scholarly manuscripts

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annajordanous/fba-solution-pack-measurement

[work in progress] An Islandora Solution pack for the DEFRA DTC archive project to allow users to create new Measurement objects (see DTC archive data model), upload CSV files to the DTC archive repository, check the validity of those CSV files against the DTC archive specified formats and generate RDF relating to the content of the CSV files.

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annajordanous/ScoreFollowingHMM

Max/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.

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annajordanous/Voice-separation--2008-

Code to take MIDI files of polyphonic music and split it up into its constituent voices, using strictly empirical methods (information on frequencies in context of a previous note). Using Matlab and the MIDI toolbox (included in this repository) by Petri Toiviainen and Tuomas Eerola, Department of Music, University of Jyväskylä, Finland: https://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/miditoolbox . This repository also contains the ICMC 2008 paper on this work and the corresponding PPT presentation.

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annajordanous/A-Fitness-Function-for-Creativity-in-Jazz-Improvisation-and-Beyond

The code featured in this academic paper from 2010 - here is the abstract of the paper Can a computer evolve creative entities based on how creative they are? Taking the domain of jazz improvisation, this ongoing work investigates how creativity can be evolved and evaluated by a computational system. The aim is for the system to work with minimal human assistance, as autonomously as possible. The system employs a genetic algorithm to evolve musical parameters for algorithmic jazz music improvisation. For each set of parameters, several improvisations are generated. The fi tness function of the genetic algorithm implements a set of criteria for creativity proposed by Graeme Ritchie. The evolution of the improvisation parameters is directed by the creativity demonstrated in the generated improvisations. From preliminary fi ndings, whilst Ritchie's criteria does guide the system towards producing more acceptably pleasing and typical jazz music, the criteria (in their current form) rely too heavily on human intervention to be practically useful for computational evaluation of creativity. In pursuing more autonomous creativity assessment, however, this system is a promising testbed for examining alternative theories about how creativity could be evaluated computationally.

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