Music in the Archives

1. David De Roure Music in the Archives: Digital Musicology as a case study in Computational Archival Science Oxford e-Research Centre Alan Turing Institute @dder

3. Setting the Scene in Scholarship: Social Machines & Citizen Science Signals & Salami Automation, Apparatus & Agency From Box 170 to the Barbican Computational Archival Science

4. Edwards,P.N.,etal.(2013)KnowledgeInfrastructures:IntellectualFrameworksand ResearchChallenges.AnnArbor:DeepBlue.http://hdl.handle.net/2027.42/97552 Knowledge Infrastructures: Intellectual Frameworks and Research Challenges

5. Digital Scholarship Social Networks More people Moremachines Distributed Computation Conventional Computation Science 2.0 Data Science Citizen Science Scientific Computing Cyberphysical Web 2.0 @dder

6. More people Moremachines Automation & Machine Learning @dder Digital Scholarship

7. Social Machines “Real life is and must be full of all kinds of social constraint – the very processes from which society arises. Computers can help if we use them to create abstract social machines on the Web: processes in which the people do the creative work and the machine does the administration... The stage is set for an evolutionary growth of new social engines. The ability to create new forms of social process would be given to the world at large, and development would be rapid.” Berners-Lee, Weaving the Web, 1999 (pp. 172–175)

9. Scientists Talk ForumImage Classification data reduction Citizen Scientists

10. In an effort to speed up classifications to cope with the large number of galaxies we expect to receive from new surveys, we've been working on ways to combine your classifications with those of machines, inspired by the idea that the combination of both automatic and human classification may be more powerful than either alone. If you choose the 'Enhanced' work flow, you will be much more likely to see the top 100 galaxies our galaxy-classifying robot thinks it needs help with in order to improve. All galaxies will be seen by at least a few volunteers to make sure we aren't missing anything. If you'd rather just see a random selection of available galaxies, choose 'Classic’. If you choose the 'Enhanced' work flow, you will be much more likely to see the top 100 galaxies our galaxy-classifying robot thinks it needs help with in order to improve. All galaxies will be seen by at least a few volunteers to make sure we aren't missing anything. If you'd rather just see a random selection of available galaxies, choose 'Classic’. https://www.zooniverse.org/projects/zookeeper/galaxy-zoo/about/research

12. StephenDownie

13. INT. VERSE VERSE VERSE VERSEBRIDGEBRIDGE OUT. ê signal understanding

14. Music Information Retrieval Evaluation eXchange • Began as MIREX in 2005 • Tasks defined by community debate • Data sets collected and/or donated • Participants submit code • Non-consumptive research • Sessions convened at the annual International Conference on Music Information Retrieval (ISMIR) StephenDownie

15. https://ccrma.stanford.edu/~benwill1/arcs/about.html

16. Community Software Supercomputer Digital Music Collections Student-sourced ground truth Community Software Linked Data Repositories Supercomputer 23,000 hours of recorded music Music Information Retrieval Community SALAMI 2009 DavidDeRoureJ.StephenDownieIchirofujinaga

17. Jordan B. L. Smith, J. Ashley Burgoyne, Ichiro Fujinaga, David De Roure, and J. Stephen Downie. 2011. Design and creation of a large-scale database of structural annotations. In Proceedings of the International Society for Music Information Retrieval Conference, Miami, FL, 555–60.

18. https://github.com/DDMAL/salami-data-public

19. StephenDownie

20. • The world of music has changed for good in the digital age. This revolution must be matched by a transformation of the means by which music is studied. • While preserving the best traditional values and practices of musicology we must take advantage of the immense opportunities offered by music information retrieval • Three parallel musicological investigations: 16th-century vocal and lute music, Wagner's leitmotifs, Musicology of the social media • Ensure sustainability and repeatability by embedding the above research activities in a framework enabling data, methods and results to be shared permanently as Linked Data • Enhance Semantic Web workflow description methods for musicology

21. TerhiNurmikko-Fuller

22. CarolinRindfleisch

24. • A workflow commons for workflow sharing, designed using Web 2.0 principles • Launched open beta in November 2007, still actively used • Largest public collection of workflows (3000+), for multiple workflow systems • 1300+ entries in Google Scholar refer to myexperiment.org • Open source, REST API, part of Open Linked Data cloud (66k triples) - lod-cloud.net • Introduced “packs” which led to Research Objects – www.researchobject.org • Workflow collection studied in scientific workflow and e-Science communities CaroleGoble www.myexperiment.org De Roure, D., Goble, C. Stevens, R. (2009) The Design and Realisation of the myExperiment Virtual Research Environment for Social Sharing of Workflows. Future Generation Computer Systems 25, pp. 561-7.

25. Notifications and automatic re-runs Machines are users too Autonomic Curation Self-repair New research? JCDL 2013

27. Fusing Audio and Semantic Technologies www.semanticaudio.ac.uk

28. The Vision: Digital Music Objects and Music Flows DMOs are rich computational objects that wrap up multiple structured digital music content types with rich metadata. This ‘musical essence’ is structured in a way that allows it to be dynamically edited, mixed and combined in the workflows of the music production landscape. KevinPageJohnPybusGrahamklyneDavidWeigl

29. Compose Perform Record Mix Distribute Discover Listen Share Pros Pro-Ams Amateurs Fans Audiences Compose Perform Record Mix Distribute Discover Listen Share Pros Pro-Ams Amateurs Fans Audiences Steve Benford

30. Composer Emily Howard creates ‘Numbers into Notes’ event format Numbers into Notes Web Software Audience Fan Amateur Semi-pro Professional Com pose Perform Record M ix Distribute Discover Listen Share Produce Consume Pro Casual Actor Activity Learner Recordings available on Soundcloud Live performance in Bethnal Green People generate music fragments Film available on YouTube Fragments used by professional composers Music for film soundtrack Composition for Tate Modern video Published on YouTube ‘Numbers into Notes’ event using software Automated reuse in SOFA remixer Spatial demo in Florence

31. numbersintonotes.net

32. GrahamKlyne

33. This piece brings together, participation, algorithmic composition andaugmentation (as a mechanism by which people can work together to augment and support a composer’s workflow). It expands upon Chamberlain’s work into compositional practices that explore autonomy and control, and builds upon the Numbers into Notes system as developed by De Roure. The piece uses the symbolism of the gift to frame parts of the interactions that have occurred in the development of the piece. Individuals are given the chance to create an algorithm. This is made into a physical entity, which is then gifted to the composer; these together are combined and used to compose a piece. The piece is then performed and given back to the audience (live), of which some members have created the original algorithms. https://soundcloud.com/alain_du_norde/the-gift-the-algorithm-beyond-autonomy-and-control The performance creates a gift, a souvenir, a memento of the experience which some of the audience members can take away. The performance also acts as a way in which we can also understand the interplay between algorithms, art, performance, provenance and participation. The gift of the algorithm: beyond autonomy and control Alan Chamberlain, David De Roure PhotoPipWillcox

35. Barbican,9Mar2019 The Eternal Golden Braid RNCM,13June2019 The Voice of the Machine RoyalInstitutionChristmas Lectures2019 Secret & Lies The Hidden Power of Maths Marcus du Sautoy Robert Laidlow Lecture 3 How can we all win? (at 57mins)

36. Milton Court Concert Hall, 2 Nov 2019 Alter | PRiSM led by Robert Laidlow Alter was premiered by the Britten Sinfonia and Marta Fontanals- Simmons, conducted by William Cole, at the Barbican.

37. Ada Lovelace Charles Babbage Bodleian Libraries 19th Century AI 21st Century AI Audience Human composer Robert Laidlow Alter Text – PRiSM team led by Robert Laidlow Commissioned by Barbican, performed by Britten Sinfonia with Marta Fontanals-Simmons, conductor William Cole, November 2019. Mezzo-soprano Marta Fontanals-Simmons prompts Images © Clay Mathematics Institute

38. Robert Laidlow interviewed by Andrew McGregor before the performance of 'Alter', a piece by PRiSM led by Robert Laidlow. Part of the event 'Ada Lovelace: Imagining the Analytical Engine', curated by Emily Howard on 2 November 2019 at Milton Hall, London. https://www.robertlaidlow.co.uk/alter

39. Some questions about the AI: If we train on copyright content, can we make the model available? Do we want to archive the training data, the models, the generated content? It’s computational archival content. Do we need to archive something if we can reproduce it?

41. dcic.umd.edu/june-20-21-2019-uk-us-computational-archival-science-cas-datathon-in-london/ Eirini Goudarouli Richard Marciano Mark Hedges Dave Beavan Pip Willcox DDeRRyan Cox Greg Jansen Sonia Ranade

42. Scholarly Primitives refer to some basic functions common to scholarly activity across disciplines, over time, and independent of theoretical orientation. These ‘self-understood’ functions form the basis for higher-level scholarly projects, arguments, statements, interpretations—in terms of our original, mathematical/philosophical analogy, axioms. John Unsworth Discovering Annotating Comparing Referring Sampling Illustrating Representing David De Roure and Pip Willcox. 2020. Scholarly Social Machines: A Web Science Perspective on our Knowledge Infrastructure. In 12th ACM Conference on Web Science (WebSci '20). Association for Computing Machinery, New York, NY, USA, 250–256. DOI:https://doi.org/10.1145/3394231.3397915

43. We become what we behold. We shape our tools and then our tools shape us. We become what we behold. We shape our research tools and then our tools shape our research. We become what we behold. We shape our archives and then our archives shape us. What do we behold in Computational Archival Science? Because that is what we will become.

44. Supported by: Theory and Practice of Social Machines (SOCIAM) EPSRC EP/J017728/2 Transforming Musicology, AHRC Fusing Audio and Semantic Technologies (FAST) EPSRC EP/L019981/1 The Alan Turing Institute ’Data Science of Music’ RNCM Centre for Practice & Research in Science & Music (PRiSM), funded by Research England Expanding Excellence in England (E3) This talk is based on talks at: ‘Computational archival science: automating the archive’, The National Archives, Sep 2018, organised by Eirini Goudarouli ‘Discovering Collections, Discovering Communities’ (DCDC) conference, Birmingham UK 2019 ACM Web Science 2020, ‘Scholarly Social Machines’, co-authored with Pip Willcox

45. RivkaGottliebperformsFib12atFASTIndustryDay AbbeyRoadStudios,October2018 Fib12,composedusingNumbersintoNotes. ReleasedonAdaLovelaceDay2018 https://soundcloud.com/david-de-roure/fib12-ada-lovelace-day-2018

Music in the Archives

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