Digital Music Research: from Music Objects to Social Machines David De Roure e-Research Centre, University of Oxford @dder
Dec 03, 2014
Digital Music Research: fromMusic Objects to Social Machines
David De Roure
e-Research Centre, University of Oxford@dder
The nature of multidisciplinary research
Structural Analysis of Music
Music as an exemplar of end-to-end digital
Social Objects and Social Machines
YES
https://xkcd.com/1289/
Richard Klavans and Kevin W. Boyack. 2009. Toward a consensus map of science. J. Am. Soc. Inf. Sci. Technol. 60, 3 (March 2009), 455-476. DOI=10.1002/asi.v60:3 http://sci.slis.indiana.edu/klavans_2009_JASIST_60_455.pdf
Pip WillcoxPip WillcoxFrom data to signal to understanding
The Problem
signal
understanding
Community Software
Supercomputer
Digital Music Collections
Student-sourced ground truth
Community Software
Linked Data Repositories
Supercomputer
23,000 hours ofrecorded music
Music InformationRetrieval Community
SALAMI
Ashley Burgoyne
salami.music.mcgill.ca
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
class structure
Ontology models properties from musicological domain• Independent of Music Information Retrieval research and signal
processing foundations• Maintains an accurate and complete description of relationships
that link them
Segment Ontology
Ben Fields, Kevin Page, David De Roure and Tim Crawford (2011) "The Segment Ontology: Bridging Music-Generic and Domain-Specific" in 3rd International Workshop on Advances in Music Information Research (AdMIRe 2011) held in conjunction with IEEE International Conference on Multimedia and Expo (ICME), Barcelona, July 2011
MIREX TASKSAudio Artist Identification Audio Onset Detection
Audio Beat Tracking Audio Tag Classification
Audio Chord Detection Audio Tempo Extraction
Audio Classical Composer ID Multiple F0 Estimation
Audio Cover Song Identification Multiple F0 Note Detection
Audio Drum Detection Query-by-Singing/Humming
Audio Genre Classification Query-by-Tapping
Audio Key Finding Score Following
Audio Melody Extraction Symbolic Genre Classification
Audio Mood Classification Symbolic Key Finding
Audio Music Similarity Symbolic Melodic Similarity
ww
w.m
usic
-ir.o
rg/m
irex
Downie, J. Stephen, Andreas F. Ehmann, Mert Bay and M. Cameron Jones. (2010). The Music Information Retrieval Evaluation eXchange: Some Observations and Insights. Advances in Music Information Retrieval Vol. 274, pp. 93-115
Music Information Retrieval Evaluation eXchange
seasr.org/meandreMeandre
chromogram
Representations
symbolic
Structural analysis
Autocorrelation
Bach
Hard Day’s Night: Self-Similarity Map
Stephen Downie
SALAMI results: a living experiment
Dav
id B
ainb
ridge
http://semanticmedia.org.uk/smam2013/
ABABCB… where A is bars 1-2, B is 3-4, C is 9-10• This is like dictionary-based compression• Or genetic programming (see also Schenkerian Analysis)
Symbolic algorithms
“Signal”Digital Audio
“Ground Truth”
Community
It’s web-like!
StructuralAnalysis
De Roure, D. Page, K.R., Fields, B., Crawford, T.,Downie, J.S. and Fujinaga, I. (2011) “An e-Research Approach to Web-Scale Music Analysis”, Philosophical Transactions of the Royal Society Series A
Sean Bechhofer
How country is my country?
Kevin Page
Sean Bechhofer, Kevin Page and David De Roure. Hello Cleveland! Linked Data Publication Of Live Music Archives. 14th International Workshop on Image and Audio Analysis for Multimedia Interactive services
Sean Bechhofer
ElEPHãT from a distance
EEBO-TCP
HathiTrust
• Smaller collection• Well understood and
described• Managed metadata• Focussed corpus• Manual transcriptions
• Extremely large collection• Incomplete understanding
of content• Variable metadata• Broad corpus• Variable quality OCR
Strengths of each informs
understanding of the other
Scholarly investigations through Worksets bridging both collections
Technical challenges• Necessary “anchors” at each “end”• Tools for dynamic alignment• Linked Data “bridging” between the collections• Creation and viewing of Worksets using this linked data
Informingfuture integration of external collections
Kevi
n Pa
ge a
nd P
ip W
illco
x
• Transforming Musicology is funded under the AHRC Digital Transformations in the Arts and Humanities scheme. It seeks to explore how emerging technologies for working with music as sound and score can transform musicology, both as an academic discipline and as a practice outside the university.
• The work is being carried out collaboratively between Goldsmiths College, Queen Mary College, Oxford University, the Oxforde-Research Centre, and Lancaster University with an international partner at Utrecht University.
• 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 investigations1. 16th-century vocal and lute music2. Wagner's leitmotifs3. Musicology of the social media
• Ensure sustainability and repeatability by embedding the above research activities in a framework enabling data, methods andresults to be shared permanently as Linked Data
• Enhance Semantic Web workflow description methods for musicology
FUSING AUDIO AND SEMANTIC TECHNOLOGIES for
INTELLIGENT MUSIC PRODUCTION AND CONSUMPTION
“Gold Standard” Music Metadata
Enhancements for musical enjoymentby home consumers
In-song browsing • learn how songs and
symphonies are structured
• e.g. find (and repeat) the guitar solo
• e.g. find vocals and enhance them
• e.g. create/locate guitar tablature
In-collection browsing • build great playlists easily: by mood
or emotion. e.g. for jogging, driving, relaxing; containing only pieces in G Major; containing Rock & Roll with orchestral strings; with a synth sound like Stevie Wonder
• discover and purchase new music, whether using Spotify or iTunes
• discover shared musical tastes
“Gold Standard” Music Metadata
Enhancements for professionals
Content owners• get instantaneous
information on trends, etc., from social media feeds
• enhance their product with exclusive artist information, locked to purchase
• distributers provide Digital Music Objects with the right bandwidth for the context and ease congestion
Recording studio workflow • engineers intelligently navigate
complex mixes• producers can apply new
sound effects to isolated elements of the music
Broadcast studio workflow • producers select content for
the radio or TV show by mood, by example or by intelligent navigation
consume
produce
composeperformcapture
distribute
Mark Sandler(plus curation, preservation, …)
Now• No production or content metadata capture – c.f. still and video cameras• Clear audio standards (e.g. 192 kHz/24 bit) but incompatible product-
specific project files• No intelligent, content-semantic automation or assistanceGoals• Capture/ compute of GSMM to drive all down-stream processes• Improved interoperability across system vendorsChallenges• Develop equipment and instruments that capture metadata (e.g mic
with time-code and GPS)• Standardised semantic, linked metadata
capture produce distribute consume
Now• Convergence in function of pro- and consumer products• No/little metadata kept• No standards, particularly in describing processes (audio effects)• Mostly PC/Mac software solutions for Digital Audio WorkstationGoals• low cost equipment, including software and tablets• assist/semi-automate (post) production• capture post-production metadata for re-engineering content, user-
customisation.Challenges• Using cloud• Standardised semantic, linked metadata• Tools & kit for automated metadata processing, capture, logging
capture produce distribute consume
Now• Different platforms & formats. Piracy. • Increasing use of IP for distribution. • Transcoding within channels, quality loss, managing multiple copiesGoals• Simpler transcoding (e.g. embedded scalability• Distribute content linked to metadata• Encrypted metadata: supports consumer while defying piracy• Digital Music ObjectChallenges• Encryption standards for metadata• Linking semantic, standardised metadata.• Aggregate metadata from up/down stream
capture produce distribute consume
Now• No context awareness, no customisation. • Some transcoding of bit-rates, #channels.• Little immersion, both intellectual and audio.• Unfulfilled desires to share, re-purpose, integrate with social mediaGoals• Modify experience to suit context• Re-balance between instruments• Seamlessly switch #channels as user context changes• Navigate collections; songs• EdutainmentChallenges• Repurposing content to match device and context
capture produce distribute consume
Nei
l Chu
e H
ong
An exemplar for software practice
• Global distributed system: software, data and processor allocation by bandwidth but also rights, copyright, …
• Realtime, streaming (cf big data)• Digital Rights Management and provenance• Algorithm IPR• Heavily app based• MIR open source community and MIREX• Non-consumptive research
Digital Music Object
Mark Sandler, Geraint Wiggins
Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. Ann Arbor: Deep Blue. http://hdl.handle.net/2027.42/97552
Research Objects
ComputationalResearch Objects
The Evolution of Research Objects
WorkflowsPacks O
AIO
RE
W3C PRO
V
Social Objects
Join the W3C Community Group www.w3.org/community/rosc
Jun Zhao
www.researchobject.org
The R Dimensions
Research Objects facilitate research that is reproducible, repeatable, replicable, reusable, referenceable, retrievable, reviewable, replayable, re-interpretable, reprocessable, recomposable, reconstructable, repurposable, reliable, respectful, reputable, revealable, recoverable, restorable, reparable, refreshable?”
@dder 14 April 2014
sci method
access
understand
new use
social
curation
Research Object
Principles
The Big Picture
More people
Mor
e m
achi
nes
Big DataBig Compute
Conventional Computation
“Big Social”Social Networks
e-infrastructure
onlineR&D
SocialMachines
deeplyaboutsociety
The
futu
re
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)
Social Machines
SOCIAM: The Theory and Practice of Social Machines is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EPJ017728/1 and comprises the Universities of Southampton, Oxford and Edinburgh. See sociam.org
Mark d’Invernohttp://goldsmiths.musiccircleproject.com/PRAISE: Performance and pRactice Agents Inspiring Social Education
The Web Observatory
Tiropanis, T., Hall, W., Shadbolt, N., De Roure, D., Contractor, N., and Hendler, J. The web
science observatory. IEEE Intelligent Systems 28, 2 (2013), 100–104.
Nigel Shadbolt et al
STORYTELLING AS A STETHOSCOPE FOR SOCIAL MACHINES
1. Sociality through storytelling potential and realization
2. Sustainability through reactivity and interactivity
3. Emergence through collaborative authorship and mixed authority
Zooniverse is a highly storified Social Machine
Facebook doesn’t allow for improvisation
Wikipedia assigns authority rights rigidly
Tarte, S. M., De Roure, D., and Willcox, P. Working out the plot: the role of stories in social machines. In Proceedings of the companion publication of the
23rd international conference on World wide web companion (2014), International World Wide Web Conferences Steering Committee, pp. 909–914.
Big data elephant versus sense-making network?
The challenge is to foster the co-constituted socio-technical system on the right i.e. a computationally-enabled sense-making network of expertise, data, models, software, visualisations and narratives
Iain Buchan
• Digital doesn’t respect disciplinary boundaries – don’t just retrofit digital inside the barriers of historic academic structures, think forward instead:– End to end digital systems– End to end semantics
• Try applying the lenses of– Social Objects– Social Machines
• Music as an exemplar for science, informing ICT strategy and future of scholarly communications
• Always ask hard questions, especially given the disruptions of increasing empowerment and automation
Take home messages
[email protected]/people/dder
@dder
SOCIAM: The Theory and Practice of Social Machines is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EPJ017728/1 and comprises the Universities of Southampton, Oxford and Edinburgh. See sociam.org
Slide and image credits: Sean Bechhofer, Iain Buchan, Neil Chue Hong, Tim Crawford, Stephen Downie, Ben Fields, Ichinaro Fujinaga, Carole Goble, Mark d’Inverno, Kevin Page, Mark Sandler, Pip Willcox, Jun Zhao.
Thanks to NEMA, SALAMI, Wf4Ever, Transforming Musicology, FAST, SOCIAM, PRAISE and all our colleagues in the ISMIR community.
Bechhofer, S., Page, K., and De Roure, D. Hello Cleveland! linked data publication of live music archives. In Image Analysis for Multimedia Interactive Services (WIAMIS), 2013 14th International Workshop on (2013), IEEE, pp. 1–4.De Roure, D. Towards computational research objects. In Proceedings of the 1st International Workshop on Digital Preservation of Research Methods and Artefacts (2013), ACM, pp. 16–19.De Roure, D., Page, K. R., Fields, B., Crawford, T., Downie, J. S., and Fujinaga, I. An e-research approach to web-scale music analysis. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369, 1949 (2011), 3300–3317.Fields, B., Page, K., De Roure, D., and Crawford, T. The segment ontology: Bridging music- generic and domain-specific. In Multimedia and Expo (ICME), 2011 IEEE International Conference on (2011), IEEE, pp. 1–6.Page, K. R., Fields, B., De Roure, D., Crawford, T., and Downie, J. S. Capturing the workflows of music information retrieval for repeatability and reuse. Journal of Intelligent Information Systems 41, 3 (2013), 435–459. (Also Reuse, remix, repeat: the workflows of mir. In ISMIR (2012), pp. 409–414.)Page, K. R., Fields, B., Nagel, B. J., O’Neill, G., De Roure, D. C., and Crawford, T. Semantics for music analysis through linked data: How country is my country? In e-Science (e-Science), 2010 IEEE Sixth International Conference on (2010), IEEE, pp. 41–48.Tarte, S. M., De Roure, D., and Willcox, P. Working out the plot: the role of stories in social machines. In Proceedings of the companion publication of the 23rd international conference on World Wide Web companion (2014), pp. 909–914.Tiropanis, T., Hall, W., Shadbolt, N., De Roure, D., Contractor, N., and Hendler, J. The web science observatory. IEEE Intelligent Systems 28, 2 (2013), 100–104.De Roure, D. Machines, methods and music: On the evolution of e-research. In High Performance Computing and Simulation (HPCS), 2011 International Conference on (2011), IEEE, pp. 8–13.
www.oerc.ox.ac.uk
[email protected]@dder