J. S. Downie, D. De Roure, K. Page.Towards Web-Scale Analysis of Musical Structure

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Towards Web-Scale Analysis of Musical Structure David De Roure J. Stephen Downie Kevin Page salami.music.mcgill.ca Ichiro Fujinaga Tim Crawford Ben Fields David Bretherton

description

J. Stephen Downie (Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign), David De Roure (Oxford e-Research Centre, University of Oxford) and Kevin Page (Oxford e-Research Centre, University of Oxford).Music Linked Data Workshop, 12 May 2011, JISC, London.

Transcript of J. S. Downie, D. De Roure, K. Page.Towards Web-Scale Analysis of Musical Structure

Page 1: J. S. Downie, D. De Roure, K. Page.Towards Web-Scale Analysis of Musical Structure

Towards Web-Scale

Analysis of Musical Structure

David De Roure

J. Stephen Downie

Kevin Page

salami.music.mcgill.ca

Ichiro Fujinaga

Tim Crawford

Ben Fields

David Bretherton

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• SALAMI == Structural Analysis of Large amounts of Music Information

• Musical analysis has traditionally been conducted by individuals and on a small scale

• Computational approach, combined with the huge volume of data now available, will

1. Deliver substantive corpus of musical analyses in common framework for music scholars and students

2. Establish a methodology and tooling so that community can sustain and enhance this resource

SALAMI Objectives

www.diggingintodata.org

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• A resource of this size empowers musicologists to approach their work in a new and different way, starting with the data, and to ask research questions that have not been possible before

• The analysis is useful in classifying different genres of music and can be used to compare different styles of composition within a composer’s works or between composers

• It can also be used to understand historical influences over time and location

Motivation

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Digital Music Collections

Student-sourced ground truth

Community Software

Linked Data Repositories

Supercomputer

23,000 hours of

recorded music

Music Information

Retrieval Community

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Ashley Burgoyne

Ground Truth

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Number of annotated pieces by genre

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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

Kevin Page and Ben Fields

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See Kevin Page

for more info…

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• Music Information Retrieval Evaluation eXchange• Began in 2005

• Tasks defined by community debate

• Data sets collected and/or donated

• Participants submit code to IMIRSEL

• Code rarely works first try

• Huge labour consumption getting programs to work

• Meet at ISMIR to discuss results

Stephen Downie

www.music-ir.org/mirex

MIREX Overview

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seasr.org/meandreMeandre

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Structural analysis processing time by different algorithms

Evaluations of 3 algorithms and human against a ground truthFPC = Frame Pair Clustering

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“Signal”Digital Audio

“Ground Truth”

Community

It’s web-like!

StructuralAnalysis

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How country is my country?

Kevin Page and Ben Fieldshttp://www.nema.ecs.soton.ac.uk/countrycountry/

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Summary

• Web-scale methodology to conduct analysis of music recordings to create resource for musicologists

• Investigating algorithms for structural analysis

• Answering new research questions, evolving new methodologies

• For more info

http://salami.music.mcgill.ca/

http://www.oerc.ox.ac.uk/people/dder

• Thanks to international teams, Internet Archive, Digging into Data Challenge, collaborators & funders