ISWC 2017 Resources track paper. The study of music is highly interdisciplinary, and thus requires the combination of datasets from multiple musical domains, such as catalog metadata (authors, song titles, dates), industrial records (labels, producers, sales), and music notation (scores). While today an abundance of music metadata exists on the Linked Open Data cloud, linked datasets containing interoperable symbolic descriptions of music itself, i.e. music notation with note and instrument level information, are scarce. In this paper, we describe the MIDI Linked Data Cloud dataset, which represents multiple collections of digital music in the MIDI standard format as Linked Data using the novel midi2rdf algorithm. At the time of writing, our proposed dataset comprises 10,215,557,355 triples of 308,443 interconnected MIDI files, and provides Web-compatible descriptions of their MIDI events. We provide a comprehensive description of the dataset, and reflect on its applications for research in the Semantic Web and Music Information Retrieval communities.

1. ‹#› Het begint met een idee
THE MIDI LINKED DATA
CLOUD
Albert Meroño-Peñuela, Rinke Hoekstra, Aldo Gangemi, Peter Bloem, Reinier de Valk,
Bas Stringer, Berit Janssen, Victor de Boer, Alo Allik, Stefan Schlobach, Kevin Page
ISWC 2017, October 23rd

2. Vrije Universiteit Amsterdam
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THE WEB MUSIC ECOSYSTEM

3. Vrije Universiteit Amsterdam
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LINKED MUSIC ON THE WEB
Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar,
Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/
Etree
See Daquino et al. 2017 (WHiSe II)
Characterizing the Landscape of
Musical Data on the Web: state of
the art and challenges

4. Vrije Universiteit Amsterdam
Symbolic music databases (MusicXML, MIDI, NIFF, MEI) are non-
interoperable
From Daquino et al.’s (WHiSe 2017):
 “Repositories and digital libraries are the most representative
resources collecting musical data. They mainly offer digitisations
of scores and lyrics (77%), published as PDF and/or JPG (40%)”
 “The more the scale of repositories increases, the less structured
formats for representing symbolic notation seem to be used and
the less depth of analysis is provided”
 “Larger collections are more likely to feature melody”
Can we find ways of increasing the level of structure of
musical data without compromising its scalability?
4
COOL, BUT…

5. Vrije Universiteit Amsterdam
 MIDI: Digital music representation protocol
> (i.e. leaving nothing to analog signals  actual instruments)
 Popular/abundant, production, standard
 Musical Instrument Digital Interface (1983)
> Universal synthesizer interface
> Roland (I. Kakehashi), Yamaha, Korg, Kawai (1981)
> Digital, fine-grained representation of musical tracks and events
> Wide range of controllers and instruments
5
MIDI

6. Vrije Universiteit Amsterdam
[ 144, 60, 100 ]
6
BUT WHAT IS MIDI?
Thanks @rumyra! https://www.youtube.com/watch?v=khsBjXKJOPs

7. Vrije Universiteit Amsterdam
[ 144, 60, 100 ]
[ 128, 60, 64 ]
7
BUT WHAT IS MIDI?
Thanks @rumyra! https://www.youtube.com/watch?v=khsBjXKJOPs

8. Vrije Universiteit Amsterdam
midi2rdf: lossless conversion of MIDI to RDF (and back)
Albert Meroño-Peñuela, Rinke Hoekstra. “The Song Remains the Same: Lossless Conversion and
Streaming of MIDI to RDF and Back”. In: 13th Extended Semantic Web Conference (ESWC 2016),
posters and demos track. May 29th — June 2nd, Heraklion, Crete, Greece (2016).
 rdf2midi, direct stream mapping
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MIDI2RDF & RDF2MIDI
https://midi-ld.github.io/

9. Vrije Universiteit Amsterdam
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MIDI SCHEMA
http://purl.org/midi-ld/midi#

10. Vrije Universiteit Amsterdam
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MIDI LINKED DATA RESOURCES
 MIDI Pieces http://purl.org/midi-ld/piece/
> Access to MIDI level triples
> Cryptographic hash for unique MIDI content
http://purl.org/midi-ld/pattern/87dd99fb346cd4c7934cb36a00868cbe
 MIDI Notes http://purl.org/midi-ld/notes/
> Type, label, octave, pitch value
 MIDI Programs http://purl.org/midi-ld/programs/
> All instruments linked to DBpedia
 MIDI Chords http://purl.org/midi-ld/chords/
> Label, quality, number of pitch classes, intervals
 Enrichments
> Provenance
> Integrated lyrics (mostly from karaoke data)
> Key (Krumhansl-Schumkler), scale degree, metric accents

11. Vrije Universiteit Amsterdam
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MIDI LINKED DATA RESOURCES
Current collections
 The largest MIDI collection on the Internet (thanks @midi_man)
 Lakh MIDI dataset (thanks @colinraffel)
 MySongBook MIDI
 Yours! https://midi-ld.github.com
 308,443 interconnected MIDI files
 10,215,557,355 triples
 Full dump, SPARQL endpoint, RESTful API

12. Vrije Universiteit Amsterdam
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ENABLING SEMANTIC WEB RESEARCH
 Data integration
> Further format interoperability: MIDI, MusicXML, NIFF, MEI
> Integration with formats of other arts: LabanXML
 Entity linking
> Audio (Spotify URIs), symbolic notation (MIDI), metadata (MusicBrainz)
> High heterogeneity, low overlap
> Challenge to entity linking algorithms
 Semantics and ontologies
> Music Ontology, Chord Ontology, Timeline Ontology
> Underspecification of musical concepts
> Reasoning
> Challenge for ontology alignment

13. Vrije Universiteit Amsterdam
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ENABLING MUSICOLOGY RESEARCH
 Analysis of chords, patterns and melodies at Web scale
> Integrating knowledge from external databases
> Historical, geographical, cultural, economic, sylistic contexts
 Everything has a URI
> Annotation tasks, workflow descriptions
 Establishing standard Web vocabularies
> Chords (iReal Pro), melodies, metadata
 Recommender systems
> Collaborative filtering, content-based feature extraction, hybrid
> Notation-based support for abstract representation of musical concepts
 Machine learning (multimodal training data, convincing samples)
 Audiolisation

14. Vrije Universiteit Amsterdam
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SPARQL-DJ
Web-based tool that finds, selects, plays, mixes, beat-
syncs and generates MIDI mashups from a very large MIDI
Linked Data collection

15. Vrije Universiteit Amsterdam
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FUTURE WORK
 Common dataspace for symbolic music notation
> Mappings for MusicXML, MEI, NIFF, and others
 Quality links to external Linked Datasets
> MusicBrainz, DBpedia, etc.
> Hybrid approaches (metadata, lyrics, incipits, MIR algorithms)
 Tools
> (Contextual) querying
> Annotation
> Workflow recording
 Materialization & performance
> HDT, LD#
> MIDI query rewriting

16. ‹#› Het begint met een idee16
THANK YOU!
@albertmeronyo
DATALEGEND.NET
CLARIAH.NL
https://midi-ld.github.io