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Researchers at UCD

Roisin Loughran

Post Doc Research Fellow Lvl I

School Of Business
Blackrock Block D
Carysfort Avenue, Blackrock
Co. Dublin

Tel: +353 1 7168093
Email: roisin.loughran@ucd.ie

Biography

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My Bachelors degree was in Electronic Engineering in UCD. As a musician with 10 years of classical training in violin and piano along with a keen interest in guitar, bass and local bands I combined my passions in music and technology by studying for a Masters in Music and Media Technology at Trinity College Dublin. After completing this, I continued my higher education into musical sound analysis by undertaking a PhD at the University of Limerick entitled `Musical Instrument Identification with Feature Selection Using Evolutionary Methods¿ under the supervision of Michael O¿Neill and Jacqueline Walker.

 

I returned to UCD in 2013 to a technical role as the IT & Media Coordinator for CASL. While there, I collaborated on a project researching speaker verification on a part-time basis. In November 2014 I returned to research full-time as part of the Applications in Evolutionary Design project (App¿Ed) under SFI. Here I am a member of the Natural Computing Research and Applications (NCRA) group who examine the application of algorithms based on naturally occurring phenomena to real-world problems. I specialise in using evolutionary methods to better understand creative problems such as music composition and music cognition.

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Professional

               

Education

Year 2001 Institution: University College Dublin
Qualification: BE Subject:
Year 2010 Institution: University of Limerick
Qualification: PhD Subject:
Year 2004 Institution: Trinity College Dublin
Qualification: MPhil Subject:
         

Publications

         

Conference Publications

Roisin Loughran, Jacqueline Walker and Michael O¿Neill (2008) The Use of Mel-frequency Cepstral Coefficients in Musical Instrument Recognition International Computer Music Conference (ICMC) [Details]
Roisin Loughran, Jacqueline Walker, Michael O¿Neill and Marion Farrell (2008) Musical Instrument Identification Using Principal Component Analysis and Multi-Layered Perceptrons the International Conference on Audio, Language and Image Processing (ICALIP) [Details]
Roisin Loughran, Jacqueline Walker, Michael O¿Neill and Marion Farrell (2008) Comparison of Features in Musical Instrument Identification Using Artificial Neural Networks Computer Music Modelling and Retrieval (CMMR) [Details]
Roisin Loughran, Jacqueline Walker and Michael O¿Neil (2009) An Exploration of Genetic Algorithms for Efficient Musical Instrument Identification Irish Signal and Systems Conference (ISSC) [Details]
Roisin Loughran, Jacqueline Walker, Michael O¿Neill and James McDermott (2012) Genetic Programming for Musical Instrument Identification EvoMUSART [Details]
Róisín Loughran, James McDermott and Michael O¿Neill (2015) Grammatical Evolution with Zipf¿s Law Based Fitness for Melodic Composition Sound and Music Computing Conference (SMC) Maynooth, Ireland, [Details]
Róisín Loughran, James McDermott, Michael O'Neill (2015) Tonality Driven Piano Compositions with Grammatical Evolution IEEE Congress on Evolutionary Computation (CEC) Sendai, Japan, [Details]
         

Dissertations/Theses

Roisin Loughran (2010) Musical Instrument Identification with Feature Selection Using Evolutionary Methods. Dissertations/Theses [Details]
                                                                             

Research

Research Interests

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My main research interests are in algorithmic composition, sound analysis, computational creativity and evolutionary computation. As a member of the Natural Computing Research and Application (NCRA) group in UCD, I am part of a team that applies algorithms based on naturally occurring phenomena to real-world problems. I specialize in applying evolutionary methods such as grammatical evolution to composing music. This raises questions, not only in relation to music composition, but also in music perception, cognition and appreciation and in subjective evaluation in general. Defining and measuring subjective evaluations in a creative domain such as music is paramount to the understanding of computational creativity. Understanding and enumerating these subjective and aesthetic criteria plays a critical role in the development of creatively intelligent and more generally artificially intelligent systems.

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