The song remains the same: Lossless conversion and streaming of MIDI to RDF and back

Albert Meroño-Peñuela, Rinke Hoekstra

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

In this demo, we explore the potential of RDF as a representation format for digital music. Digital music is broadly used today in many professional music production environments. For decades, MIDI (Musical Instrument Digital Interface) has been the standard for digital music exchange between musicians and devices, albeit not in a Web friendly way. We show the potential of expressing digital music as Linked Data, using our midi2rdf suite of tools to convert and stream digital music in MIDI format to RDF. The conversion allows for lossless round tripping: we can reconstruct a MIDI file identical to the original using its RDF representation. The streaming uses an existing, novel generative audio matching algorithm that we use to broadcast, with very low latency, RDF triples of MIDI events coming from arbitrary analog instruments.

LanguageEnglish
Title of host publicationThe Semantic Web - ESWC 2016 Satellite Events, Revised Selected Papers
PublisherSpringer/Verlag
Pages194-199
Number of pages6
Volume9989 LNCS
ISBN (Print)9783319476018
DOIs
Publication statusPublished - 2016
Event13th International Conference on Semantic Web, ESWC 2016 - Heraklion, Crete, Greece
Duration: 29 May 20162 Jun 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9989 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference13th International Conference on Semantic Web, ESWC 2016
CountryGreece
CityHeraklion, Crete
Period29/05/162/06/16

Fingerprint

Musical instruments
Streaming
Music
Linked Data
Matching Algorithm
Broadcast
Convert
Latency
Analogue

Keywords

  • Linked data
  • MIDI
  • Music streams
  • RDF

Cite this

Meroño-Peñuela, A., & Hoekstra, R. (2016). The song remains the same: Lossless conversion and streaming of MIDI to RDF and back. In The Semantic Web - ESWC 2016 Satellite Events, Revised Selected Papers (Vol. 9989 LNCS, pp. 194-199). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9989 LNCS). Springer/Verlag. https://doi.org/10.1007/978-3-319-47602-5_38
Meroño-Peñuela, Albert ; Hoekstra, Rinke. / The song remains the same : Lossless conversion and streaming of MIDI to RDF and back. The Semantic Web - ESWC 2016 Satellite Events, Revised Selected Papers. Vol. 9989 LNCS Springer/Verlag, 2016. pp. 194-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{fa13d88f82b7462283a1cc9b8b232f44,
title = "The song remains the same: Lossless conversion and streaming of MIDI to RDF and back",
abstract = "In this demo, we explore the potential of RDF as a representation format for digital music. Digital music is broadly used today in many professional music production environments. For decades, MIDI (Musical Instrument Digital Interface) has been the standard for digital music exchange between musicians and devices, albeit not in a Web friendly way. We show the potential of expressing digital music as Linked Data, using our midi2rdf suite of tools to convert and stream digital music in MIDI format to RDF. The conversion allows for lossless round tripping: we can reconstruct a MIDI file identical to the original using its RDF representation. The streaming uses an existing, novel generative audio matching algorithm that we use to broadcast, with very low latency, RDF triples of MIDI events coming from arbitrary analog instruments.",
keywords = "Linked data, MIDI, Music streams, RDF",
author = "Albert Mero{\~n}o-Pe{\~n}uela and Rinke Hoekstra",
year = "2016",
doi = "10.1007/978-3-319-47602-5_38",
language = "English",
isbn = "9783319476018",
volume = "9989 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer/Verlag",
pages = "194--199",
booktitle = "The Semantic Web - ESWC 2016 Satellite Events, Revised Selected Papers",

}

Meroño-Peñuela, A & Hoekstra, R 2016, The song remains the same: Lossless conversion and streaming of MIDI to RDF and back. in The Semantic Web - ESWC 2016 Satellite Events, Revised Selected Papers. vol. 9989 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9989 LNCS, Springer/Verlag, pp. 194-199, 13th International Conference on Semantic Web, ESWC 2016, Heraklion, Crete, Greece, 29/05/16. https://doi.org/10.1007/978-3-319-47602-5_38

The song remains the same : Lossless conversion and streaming of MIDI to RDF and back. / Meroño-Peñuela, Albert; Hoekstra, Rinke.

The Semantic Web - ESWC 2016 Satellite Events, Revised Selected Papers. Vol. 9989 LNCS Springer/Verlag, 2016. p. 194-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9989 LNCS).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - The song remains the same

T2 - Lossless conversion and streaming of MIDI to RDF and back

AU - Meroño-Peñuela, Albert

AU - Hoekstra, Rinke

PY - 2016

Y1 - 2016

N2 - In this demo, we explore the potential of RDF as a representation format for digital music. Digital music is broadly used today in many professional music production environments. For decades, MIDI (Musical Instrument Digital Interface) has been the standard for digital music exchange between musicians and devices, albeit not in a Web friendly way. We show the potential of expressing digital music as Linked Data, using our midi2rdf suite of tools to convert and stream digital music in MIDI format to RDF. The conversion allows for lossless round tripping: we can reconstruct a MIDI file identical to the original using its RDF representation. The streaming uses an existing, novel generative audio matching algorithm that we use to broadcast, with very low latency, RDF triples of MIDI events coming from arbitrary analog instruments.

AB - In this demo, we explore the potential of RDF as a representation format for digital music. Digital music is broadly used today in many professional music production environments. For decades, MIDI (Musical Instrument Digital Interface) has been the standard for digital music exchange between musicians and devices, albeit not in a Web friendly way. We show the potential of expressing digital music as Linked Data, using our midi2rdf suite of tools to convert and stream digital music in MIDI format to RDF. The conversion allows for lossless round tripping: we can reconstruct a MIDI file identical to the original using its RDF representation. The streaming uses an existing, novel generative audio matching algorithm that we use to broadcast, with very low latency, RDF triples of MIDI events coming from arbitrary analog instruments.

KW - Linked data

KW - MIDI

KW - Music streams

KW - RDF

UR - http://www.scopus.com/inward/record.url?scp=84994491817&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84994491817&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-47602-5_38

DO - 10.1007/978-3-319-47602-5_38

M3 - Conference contribution

SN - 9783319476018

VL - 9989 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 194

EP - 199

BT - The Semantic Web - ESWC 2016 Satellite Events, Revised Selected Papers

PB - Springer/Verlag

ER -

Meroño-Peñuela A, Hoekstra R. The song remains the same: Lossless conversion and streaming of MIDI to RDF and back. In The Semantic Web - ESWC 2016 Satellite Events, Revised Selected Papers. Vol. 9989 LNCS. Springer/Verlag. 2016. p. 194-199. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-47602-5_38