Music Objects to Social Machines

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

david.deroure@oerc.ox.ac.ukwww.oerc.ox.ac.uk/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

david.deroure@oerc.ox.ac.uk@dder