DaCaRyH (Data science for the study of calypso-rhythm through history)

Lead Research Organisation: Queen Mary, University of London
Department Name: Sch of Electronic Eng & Computer Science


DaCaRyH (Data science for the study of calypso-rhythm through history) is a collaboration of ethnomusicologists and archivists in France, and data scientists and composers in the UK. DaCaRyH has 3 objectives: 1) to enrich the domain of ethnomusicology by integrating data science and music information retrieval (MIR) methods into ethnomusicological archives and research practices; 2) to enrich the domains of data science and MIR by integrating ethnomusicological use cases into the practice of the research and development of intelligent systems; 3) to study the concept of musical style through a comparative diachronic analysis of a music corpus, and to transform the features extracted from the same corpus into new styles. DaCaRyH is aligned primarily with "'The Digital Age' and its effects on tangible and intangible heritage", and secondarily with "representations and uses of the past." DaCaRyH will work specifically with the music tradition of the steel band calypso. This provides focus on a variety of real and challenging ethnomusicological questions, which in turn drive the development of data science and MIR technologies. DaCaRyH helps pave the way to "big cultural data," or the analysis of human culture at scales not possible without computational methods. DaCaRyH involves the Research Center for Ethnomusicology (CREM-LESC, France), and the Centre for Digital Music (C4DM, Queen Mary University of London, UK). CREM-LESC offers access to a large ethnomusicologic recordings database accessible worldwide through an online platform. C4DM is a world-leading group of specialists in data science applied to music. DaCaRyH will result in: two journal submissions (one in the respective fields of the PIs), a call for a special journal issue concerning cultural studies and data science, a music composition and performance project involving the use of the tools developed in DaCaRyH, and improved functionality integrated with the CREM-LESC ethnomusicological recordings archive.


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Title The folk-rnn Session Book, Volume 1 of 10 
Description The 3,000 tunes in this session volume have been generated entirely by a computerised system (named folk-rnn) trained on 23,636 tunes contributed by users of thesession.org. Each tune appears as ABC and in staff notation, and is hyperlinked to a synthesised realisation. Even though this work is not focused directly on calypso style music, it is a preliminary investigation of a music practice for which we have enough high level data. 
Type Of Art Composition/Score 
Year Produced 2016 
Impact A selection of transcriptions was performed by Torbjörn Hultmark (http://hultmark.me) on November 18, 2016 at "C4DM presents A brief evening of electroacoustic music" at QMUL: https://www.youtube.com/watch?v=4kLxvJ-rXDs These transcriptions will also form material for upcoming workshops with session musicians. 
URL https://highnoongmt.wordpress.com/2016/09/12/folk-rnn-session-tunes-volume-1-of-10/
Description The representation of music data has a surprising impact on the quality of its computational modelling. We have studied two models, each built using a different representation of the same data: one model we build with high-level textual transcriptions, and the other is the Google model (magenta, basic rnn) built with low-level MIDI representations. Through expert elicitation we have found that the models built using the high-level textual representation demonstrate much more success when it comes to generation of music data having expected conventions. We are currently designing a listening test to more formally test this difference.
Exploitation Route Our software models are freely available: https://github.com/IraKorshunova/folk-rnn
Sectors Creative Economy,Education,Culture, Heritage, Museums and Collections
Description We are currently exploiting our findings on music representations to engage audiences with new music. For instance, an upcoming workshop (http://www.insideoutfestival.org.uk/2017/events/folk-music-composed-by-a-computer/) will demonstrate to the general public how computers and machine learning can be used to augment music traditions. This involves a trio of musicians who are learning tunes composed by a computer.
First Year Of Impact 2016
Impact Types Cultural
Description Autumn concert 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Public/other audiences
Results and Impact Concert of electronic and computer music, some of which result from project outcomes.
Year(s) Of Engagement Activity 2016
URL https://sites.google.com/site/c4dmconcerts1617/home/fixedmedia/brief