▶ Digital Musicology

 Digital Musicology

Applied computational and informatics methods for enhancing musicology

  • Convener: Kevin Page
  • Hashtag: #digitalmusicology and #DHOxSS
  • Computers: Students are not required to bring their own laptops for this workshop. Laptop computers will be provided by DHOxSS

Abstract

A wealth of music and music-related information is now available digitally, offering tantalizing possibilities for digital musicologies. These resources include large collections of audio and scores, bibliographic and biographic data, and performance ephemera -- not to mention the ‘hidden’ existence of these in other digital content. With such large and wide ranging opportunities come new challenges in methods, principally in adapting technological solutions to assist musicologists in identifying, studying, and disseminating scholarly insights from amongst this ‘data deluge’.

This workshop provides an introduction to computational and informatics methods that can be, and have been, successfully applied to musicology. Many of these techniques have their foundations in computer science, library and information science, mathematics and most recently Music Information Retrieval (MIR); sessions are delivered by expert practitioners from these fields and presented in the context of their collaborations with musicologists, and by musicologists relating their experiences of these multidisciplinary investigations.

The workshop comprises a series of lectures and hands-on sessions, supplemented with reports from musicology research exemplars. Theoretical lectures are paired with practical sessions in which attendees are guided through their own exploration of the topics and tools covered. Laptops will be loaned to attendees with the appropriate specialised software installed and preconfigured.

Timetable

Time

Monday

Tuesday

Wednesday

Thursday

Friday

11:00 - 12:30

Welcome and housekeeping
Kevin Page

Overview of the Digital Musicology Workshop
Kevin Page

Roundtable introduction from attendees
Chair: Tim Crawford

Digital Musicology: a personal perspective
Tim Crawford

An Introduction to Music Information Retrieval: musicological implications
J. Stephen Downie

Big Data and Other Digital Strategies for Historical Musicologists
Stephen Rose

Representing musicological knowledge on the Web using Linked Data
Kevin Page

An overview of software and data management best practice
David Weigl

Historical musicology and digital cataloguing: achievements and possibilities
Joanna Bullivant

Introduction to Computer-aided Music Psychology
Klaus Frieler

 

[until 13:00]

A case study in Early Music, from digitisation to Linked Data: experiences from EMO, ECOLM, SLICKMEM, and SLoBR
Tim Crawford, David Lewis, David Weigl, and Kevin Page

Describing music performance and interpretation: digitally researching Wagner and the leitmotif
Carolin Rindfleisch, Kevin Page, and David Weigl

Lunch

Venue: St Anne's College, Dining Room

14:00 - 16:00

Using computers to analyse recordings: An introduction to signal processing
Chris Cannam and Ben Fields

[Hands on] Using computers to analyse recordings: Practical feature extraction for musicology
Chris Cannam and Ben Fields

Training computers automatically to recognise patterns in recordings: Practical machine learning
Ben Fields and J. Stephen Downie

 

Digitised Notated Music: hands on with MEI and MusicXML
Richard Lewis, David Lewis, and David Weigl

[starts at 14:30]

Computer processing of digital notated music: hands on with music21
"Working with symbolic music data"

Richard Lewis and David Lewis

The challenges and opportunities of finding music and music scholarship in the 4.6 billion pages of the HathiTrust Digital Library
J. Stephen Downie

In Concert: towards a collaborative digital archive of musical ephemera
Rachel Cowgill

16:30 - 17:30

[Hands on] Using computers to analyse recordings [Continued]
Chris Cannam and David Weigl

Using computer analyses to index and find recordings: Feature search and retrieval
Ben Fields

Methods for analysing large-scale resources and big music data
Tillman Weyde

Web-scale analysis of music: lessons from the SALAMI project
"Experiences in ground truth, big data, and structural analysis"

David De Roure and J. Stephen Downie

Computer processing of digital notated music: hands on with music21 [Continued]

Round table discussion: applied digital musicology in your research
Rachel CowgillTim CrawfordJ. Stephen DownieDavid LewisKevin Page, and Carolin Rindfleisch

 

Schedule Details

Monday

11:00 - 12:30

Welcome and housekeeping
Kevin Page
 

Overview of the Digital Musicology Workshop
Kevin Page

Over the coming week the Digital Musicology workshop at DHOxSS 2016 will introduce a wide variety of practical and theoretical digital techniques and illustrate their use within a number of musicology studies. Of course, in one week we can only scratch the surface of a myriad of methods and investigations - this talk contextualises the forthcoming lectures as representative of wider study, setting the scene within the wider digital musicology landscape.
 

Roundtable introduction from attendees
Chair: Tim Crawford

 

Digital Musicology: a personal perspective
Tim Crawford
 

An Introduction to Music Information Retrieval: musicological implications
J. Stephen Downie

Computational tools, such as those of music information retrieval (MIR), are being enhanced and adapted to the needs of musicologists. These offer new, rapid and effective ways of investigating large collections of music in audio or score form. Recent advances in Web technology also allow researchers to record and share their results and working methods in a sustainable way, so their methods can be easily altered in the light of new knowledge or re-used on new data. These introductory lectures offer two personal distinct but complementary views from leading researchers in their fields on how these technological innovations can be brought to bear within musicology.

14:00 - 16:00

Using computers to analyse recordings
An introduction to signal processing
Chris Cannam

This session, and the following hands-on session, introduces the basics of computational treatment of recordings of music, which are based on the concept of ‘features’ derivable from this ‘signal’ by suitable processing. The hands-on session will expose you to software for extracting features from recordings, visualising those features, and will help you understand how features relate to perceptual and musical concepts.
 

[Hands on] Using computers to analyse recordings
Practical feature extraction for musicology
Chris Cannam and Ben Fields

16:30 - 17:30

[Hands on] Using computers to analyse recordings [Continued]
Practical feature extraction for musicology
Chris Cannam and Ben Fields
 

Using computer analyses to index and find recordings
Feature search and retrieval

Ben Fields

Having previously covered the extraction of features from musical recordings, in this session you will be introduced to the technique of using geometrical distance to quantify the similarity between sets of features, and we will relate application of that technique to the task of finding recordings of interest within a larger collection.

 

Tuesday

11:00 - 12:30

Big Data and Other Digital Strategies for Historical Musicologists
Stephen Rose

This session introduces a range of digital strategies for researching music history. It focuses on the analysis, manipulation and visualisation of high-volume open data, particularly music-bibliographical datasets such as RISM (Répertoire international des sources musicales, https://opac.rism.info/index.php?id=8&L=0) and the British Library’s metadata for printed music (www.bl.uk/bibliographic/datafree.html). It also suggests ways to create a symbiosis between recent digital techniques and the older methods of historical musicology.

14:00 - 16:00

Training computers automatically to recognise patterns in recordings
Practical machine learning
Ben Fields and J. Stephen Downie

When analysing a large corpus of audio, a limiting factor is time: it is not practical to find patterns in a very large collections of audio by just listening. So-called ’machine learning’ techniques offer a means around this limit. In this session we will show you how to use modern machine learning techniques to distill out patterns in large collections of audio, without exhaustive human audition.

16:30 - 17:30

Methods for analysing large-scale resources and big music data
Tillman Weyde

This session will take the tools of the last few sessions and consider the effects and consequences of scale. We will look at how you can manage and mitigate the problems of working with very large amounts of data. We will explore techniques that work best at this scale, using music collections from the British Library to define explore, analyse and compare large datasets across historic, cultural, and musical dimensions.
 

Wednesday

11:00 - 12:30

Representing musicological knowledge on the Web using Linked Data
Kevin Page

The Semantic Web can be thought of as an extension of the WWW in which sufficient meaning is captured and encoded such that computers can automatically match, retrieve, and link resources across the internet that are related to each other. In a scholarly context this offers significant opportunities for publishing, referencing, and re-using digital research output. In this session we introduce the principles and technologies behind this ‘Linked Data’, illustrated through examples from musicological study.
 

An overview of software and data management best practice
David Weigl

Revision control refers to a set of practices to track and control changes to your project files. Learn how to manage, revise, and collaborate on digital documents; how to revert files back to a previous state; and how to see when a particular change was introduced, and who was responsible.

14:00 - 16:00

Digitised Notated Music: hands on with MEI and MusicXML
Richard Lewis, David Lewis, and David Weigl

There are two broad domains of digitised music: audio and so-called symbolic, which includes encodings of music notation. In this session we introduce two music notation formats: MEI and MusicXML. We learn the models of music notation they employ, the text critical apparatus they provide, and how to prepare documents in these formats.

16:30 - 17:30

Web-scale analysis of music: lessons from the SALAMI project
Experiences in ground truth, big data, and structural analysis
David De Roure and J. Stephen Downie

Musical analysis has traditionally been conducted by individuals, and on a small scale. In the SALAMI (Structural Analysis of Large Amounts of Music Information) project, music students performed structural analyses over a substantial music corpus in order to deliver a “ground truth”, and this was coupled with computational techniques established in the global music information retrieval research community. SALAMI exemplifies current social and "big data” approaches to take advantage of the huge volume of recorded music content data now available.
 

Thursday

11:00 - 12:30

Historical musicology and digital cataloguing: achievements and possibilities
Joanna Bullivant

MerMEId (Metadata Editor and Repository for MEI Data) is a tool allowing historical musicologists to collaborate with coders and developers to create digital resources of new clarity and accessibility. This talk will discuss the achievements of using the software in relation to the Oxford project ‘Delius, Modernism, and the Sound of Place’, and will outline challenges and further possibilities for creating digital resources that have arisen from this research.
 

Introduction to Computer-aided Music Psychology
Klaus Frieler

In the last decade, significant steps towards more user-friendly and reliable software tools for musicological research have been made. This offers opportunities for new experimental paradigms and methodologies that have been rarely employed in the past due to the large efforts involved. For example, experiments using a production paradigm are very time-consuming due to the necessary transcription step, which can be now significantly sped up using semi-automatic approaches. Moreover, modern computer-based methods allow characterising and comparing musical stimuli and experimental outcomes in a much more comprehensive, objective and flexible way. Likewise, corpus-based methods are a promising approach for incorporating aspects of music cultural background into cognitive models. Hence, these new tools have potential to boost the development of cognitive and other music psychological models and to improve experimental productivity. In this introductory presentation, an overview of available tools, techniques and user-scenarios will be given and some underlying concepts explained using various examples from my own and other people's work.

[NOTE: Lunch from 13:00 - 14:30]

14:30 - 16:00

Computer processing of digital notated music: hands on with music21
Working with symbolic music data
Richard Lewis and David Lewis

Given a corpus of digital musical documents, how can we explore its contents? In this session we introduce the music21 toolkit which allows us to search for patterns in such music corpora and to prepare reproducible analytic tools. We learn its specialist query language and some basic Python programming techniques.

16:30 - 17:30

Computer processing of digital notated music: hands on with music21 [Continued]
 

Friday

11:00 - 12:30

A case study in Early Music, from digitisation to Linked Data: experiences from EMO, ECOLM, SLICKMEM, and SLoBR
Tim Crawford, David LewisKevin Page and David Weigl

We base our presentation on our experiences with a large collection of images of historical music prints (EMO). Using optical recognition methods, we encoded a representative test-set automatically. We shall describe what further work is needed to enable useful and interesting searches, comparisons and other musical investigations on incorporating both the resulting corpus and relevant external resources.
 

Describing music performance and interpretation: digitally researching Wagner and the leitmotif
Carolin Rindfleisch, Kevin Page, and David Weigl

How are Wagner’s Music Dramas heard, seen and interpreted in different cultural and historical situations? Reaching from systematising the huge corpus of audience-aimed introductory literature about Wagner’s Ring, to digitally capturing characteristics of a certain performance, to new ways of structuring, presenting and linking our own interpretations in a digital environment, this session presents a variety of possibilities which Digital Musicology holds for this particular case study.

14:00 - 16:00

The challenges and opportunities of finding music and music scholarship in the 4.6 billion pages of the HathiTrust Digital Library
J. Stephen Downie

Very large collections of digital materials have the potential to transform musicology in both theory and practice. However, large corpora such as the HathiTrust Digital Library create as many challenges as opportunities. This session explores these challenges and highlights work being done to maximize the benefits.
 

In Concert: towards a collaborative digital archive of musical ephemera
Rachel Cowgill

With the cultural turn in musicology, scholars’ attention has shifted to traditions and cultures of performance and to related collections of ephemera. Traditionally overlooked, such material can yield rich, complex, and highly structured data with the potential to inform our understanding of taste, canon formation, musicians’ careers, the development of institutions, and the socio-economic contexts within which live music has been presented to audiences. This session reports on a project designed to gather, systematize, and interrogate data of this nature, and proposes new ways of writing and thinking about performance history.

16:30 - 17:30

Round table discussion: applied digital musicology in your research
Rachel Cowgill, Tim Crawford, J. Stephen Downie, David Lewis, Kevin Page, and Carolin Rindfleisch