An overview of Semantic Web activities in the OMRAS2 projectgyorgyf/files/papers/Fazekas2010jnmr.pdf · project † Gy¨orgy Fazekas ... (Musicbrainz, Magnatune14 and Ja-mendo15)

An overview of Semantic Web activities in the OMRAS2project†

Gyorgy Fazekas‡1, Yves Raimond2, Kurt Jacobson1, and Mark Sandler1

1Queen Mary University of London, Centre for Digital Music2BBC Future Media & Technology

Abstract

The use of cultural information is becoming increasingly important in mu-sic information research, especially in music retrieval and recommendation.While this information is widely available on the Web, it is most commonlypublished using proprietary Web APIs. The Linked Data community is aim-ing at resolving the incompatibilities between these diverse data sources bybuilding a Web of data using Semantic Web technologies. The OMRAS2project has made several important contributions to this by developing anontological framework and numerous software tools, as well as publishingmusic related data on the Semantic Web. These data and tools have foundtheir use even beyond their originally intended scope. In this paper, we firstprovide a broad overview of the Semantic Web technologies underlying thiswork. We describe the Music Ontology, an open-ended framework for com-municating musical information on the Web, and show how this frameworkcan be extended to describe specific sub-domains such as music similarity,content-based audio features, musicological data and studio production. Wedescribe several data-sets that have been published and data sources thathave been adapted using this framework. Finally, we provide applicationexamples ranging from software libraries to end user Web applications.

Introduction

From the management of personal media collections to the construction of large contentdelivery services, information management is a primary concern for multimedia-related tech-nologies. However, until recently, solutions that have emerged to solve these concerns wereexisting in isolation. For example, large online databases such as Musicbrainz1, encyclopedicsources like Wikipedia2, and personal music collection management tools such as iTunes3

†Manuscript published in the Journal of New Music Research (JNMR) Vol. 39, No. 4, pp. 295–310.‡Correspondence should be addressed to [email protected]://musicbrainz.org/2http://www.wikipedia.org/3http://www.apple.com/itunes/

SEMANTIC WEB ACTIVITIES IN THE OMRAS2 PROJECT 2

or Songbird4 do not interact with each other, although they can deal with similar kind ofdata. Consequently, information managed by one of these solutions may not benefit frominformation held by any of the other solutions.

This problem becomes acute when narrowing our view to the exchange of results betweenmusic technology researchers. If providing access to content-based feature extraction throughweb services is a first step (McEnnis, McKay, & Fujinaga, 2006; Ehmann, Downie, & Jones,2007), the results they produce must be interlinked with other data sources in order for themto be meaningful. Returning a set of results about a particular digital audio item is useless,unless we know what has been processed and how. (Raimond & Sandler, 2008)

The OMRAS2 project has made several key contributions in creating a distributed musicinformation environment linking music-related data held by previously independent systems.In order to achieve this goal, we used a set of Web standards, often referred to as SemanticWeb technologies.

In the rest of this paper we first give an overview of Web standards mentioned above, andshow how they can be used to create a ‘Web of data’—a distributed, domain-independent,web-scale database. We then describe the Music Ontology, enabling the publishing of music-related information on the Web within a unified framework. Next, we show how this frame-work can be applied to problems raised by the music information retrieval (MIR) researchcommunity, and describe how it is used to produce more interoperable and more reproducibleresearch data. We also describe several music-related datasets published and interlinkedwithin this framework, and discuss a number of software tools developed within OMRAS2for these purposes. Finally, we describe applications making use of these data and provideexamples of queries some of these tools can answer.

An Introduction to Semantic Web Technologies

One of the key enabling concepts in the success of the World Wide Web is the UniformResource Identifier or URI. It solves the problem of identifying and linking resources (webpages, data, or services) in a simple and e!cient manner. Together with the access mechanismof HTTP5, it enables the formation of a large interlinked network of documents: the Web aswe know and use it today. However, this infrastructure is not yet used as widely and e"ectivelyas it could be. In particular, the flow of data and access to networked services are cluttered byincompatible formats and interfaces. The vision of the Semantic Web (Berners-Lee, Handler,& Lassila, 2001) is to resolve this issue, in the wider context of bringing intelligence to theWeb, by creating a “Giant Global Graph” of machine-interpretable data.

Since information on the Web can stand for just about anything, developers of the Se-mantic Web are faced with a major challenge: How to represent and communicate diverseinformation so that it can be understood by machines? The answer lies in standardising howinformation is published rather than trying to arrange all human knowledge into rigid datastructures. Based on this recognition, several remarkable yet simple technologies emergedpromoting the development of the Semantic Web.

Creating interoperable online services for semantic audio analysis, and the recognitionof various musical factors are among the main objectives of the OMRAS2 project. We be-lieve that this musical information is just as diverse as information expressed on the generalWeb. Moreover, we cannot presume to plan for all possible uses of our system components.

4http://www.getsongbird.com/5The Hypertext Transfer Protocol provides basic methods for obtaining resources identified by HTTP URIs.


Therefore, we need data structures that are interoperable with other systems, and extensibleeven by end-users. This poses problems similar to building the Semantic Web itself, hencethe development of Semantic Web ontologies and the use of technologies produced in thatcommunity became key elements in the project.

RDF, Ontologies and Linked Data

Anything can be identified using a Web URI, not just a document: a person, a particularperformance, a composer, a digital signal, and so on. These Web resources may have multipleassociated representations. For example, a URI identifying a particular composer can beassociated with an HTML page giving some information about him in English, another pagegiving the same information in French, or a page suited for a mobile device. The web aspectcomes into place when other web identifiers are mentioned within such representations. Forexample, the HTML representation of a composer might link to an URI identifying one ofher compositions. When these representations are structured, they provide explicit machine-processable information.

The Resource Description Framework6 (RDF) allows for such structured representations tobe made. It expresses statements about web resources in the form of triples : subject, predicateand object. When these representations quote other resource identifiers, they enable access tocorresponding structured representations, creating a Web of structured data. For example, theURI http://www.bbc.co.uk/music/artists/10000730-525f-4ed5-aaa8-92888f060f5f#artist iden-tifies a particular artist. Two possible representations of that URI are available. One is inHTML and can be rendered for human consumption through a traditional web browser; theother is structured and machine readable, holding explicit statements about this artist. TheRDF representation, requested via the HTTP Accept header, holds among others the RDFstatements shown in the example of listing 1.

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

@prefix owl: <http://www.w3.org/2002/07/owl#> .

@prefix mo: <http://purl.org/ontology/mo/> .

<http://www.bbc.co.uk/music/artists/10000730-525f-4ed5-aaa8-92888f060f5f#artist>

rdf:type mo:MusicArtist ;

owl:sameAs <http://dbpedia.org/resource/Bat_for_Lashes> .

Listing 1 Linking two resources representing a music artist.

In our example, the explicitly written URI identifies a web resource representing an artist.We make two statements (‘triples’) about this artist. The first triple, where the predicaterdf:type and object mo:MusicArtist are written using the namespace prefix notation7, ex-presses the fact that this resource is a music artist. The second triple after the semicolon8

6Resource Description Framework specification: http://www.w3.org/RDF/7Such URI references are expanded using a namespace declaration after a @prefix directive like the ones in

our example. A prefix can also remain empty, in which case it is bound to the local namespace of the file. Inthe rest of the paper namespace prefixes will be omitted for brevity.

8The semicolon can be used to group RDF statements about the same resource.


refers to the same resource; our artist. We can then follow the owl:sameAs link to a re-source within DBpedia9, which holds structured data extracted from Wikipedia, or follow therdf:type link to get more information about what mo:MusicArtist means.

All RDF examples in this paper are written in RDF/Turtle10. Other common serialisationformats include RDF/XML11 and the conceptually broader Notation 3 (N3) language12 whichalso allows for the representation of logical rules.

In itself, RDF is a conceptual data model which provides the flexibility and modularityrequired for publishing diverse semi-structured data—that is, just about anything on theSemantic Web. RDF is also the basis for publishing extensible data schema through the useof OWL (Web Ontology Language). For example, when accessing a structured representationof the mo:MusicArtist resource we get an OWL document detailing di"erent concepts inthe music domain, as well as the relationships between them (e.g. an artist has a name andcan be involved in a performance) and links to concepts defined within other ontologies, forexample, the fact that mo:MusicArtist subsumes foaf:Agent13.

Linking Open Data

The open data movement aims at making data freely available to everyone. The publisheddata sources cover a wide range of topics: from music (Musicbrainz, Magnatune14 and Ja-mendo15) to encyclopedic information (Wikipedia) or bibliographic information (Wikibooks16,DBLP bibliography17).

A growing number of datasets are published by the Linking Open Data community cov-ering a wide range of topics (Figure 1). For instance, the DBpedia (Auer et al., 2007) projectextracts structured information from fact boxes within the Wikipedia community-edited en-cyclopedia. The Geonames dataset18 exposes structured geographic information. The BBCdatasets (Kobilarov et al., 2009) cover a wide range of information, from programmes19 toartists and reviews20. We contributed to the ‘Linking Open Data on the Semantic Web’ com-munity project (Bizer, Heath, Ayers, & Raimond, 2007) of the W3C Semantic Web Educationand Outreach group21, which aims at making such data sources available on the Semantic Weband creating links between them using the technologies described in the previous sections.

Creating bridges between previously independent data sources paves the way towards alarge machine-processable data web, gathering interlinked Creative Commons licensed con-tent22, encyclopedic information, domain-specific databases, taxonomies, cultural archives,and so on.

9DBpedia project: http://dbpedia.org/10http://www.w3.org/TeamSubmission/turtle/11http://www.w3.org/TR/REC-rdf-syntax/12Notation 3 language and syntax: http://www.w3.org/DesignIssues/Notation3.html13Here, x subsumes y if all elements of x are also elements of y.14http://magnatune.com/15http://www.jamendo.com16http://wikibooks.org/17http://dblp.uni-trier.de/18http://geonames.org/19http://www.bbc.co.uk/programmes20http://www.bbc.co.uk/music21http://www.w3.org/2001/sw/sweo/22http://creativecommons.org/


Figure 1. Datasets published by the Linking Open Data community, September 2010. Each nodecorresponds to a particular dataset. Diagram by Richard Cyganiak, and available on-line at http://richard.cyganiak.de/2007/10/lod/

Accessing and Publishing Linked Data

A standard way of accessing information is an important aspect of Linked Data besidesits representation in RDF. A recent W3C recommendation, the SPARQL23 Protocol andRDF Query Language allows complex joins of disparate RDF databases in a single query. AWeb interface executing these queries is commonly referred to as a SPARQL end-point. Thelanguage can be used in a multitude of ways. In the simplest case, the query — consistingof triple patterns — is matched against a database. Results are then composed of variablebindings of matching statements based on a select clause specified by the user. For example,the query shown in listing 2 retrieves all triples about the DBpedia resource Bill Evans. Inthis example, the HTTP URI identifies the artist in DBpedia’s database. The terms startingwith a question mark are free variables that are matched when the query is evaluated.

SELECT ?predicate ?object

WHERE { <http://dbpedia.org/resource/Bill_Evans> ?predicate ?object . }

Listing 2 A simple SPARQL query.

23http://www.w3.org/TR/rdf-sparql-query/


Using SPARQL is the easiest way of accessing the semantic web from an application,while creating an end-point is a standard way of publishing data. Most modern programminglanguages have SPARQL libraries, and several open-source RDF stores24 are available forcreating an end-point. The standardisation and increasing support of SPARQL promotes theadoption of RDF itself as a prevailing metadata model and language.

Using the Music Ontology Framework

In this section, we review the Music Ontology framework. Rather than focusing on spec-ifications and design decisions already provided elsewhere (Raimond, Abdallah, Sandler, &Frederick, 2007), we argue for common ontology-based information management in MIR, andshow how our framework can be used to describe diverse music related information. Thisincludes the composition and music publishing workflow, as well as editorial information, mu-sicological data, and content-based features of audio. The penultimate section of the paperdiscusses various musical applications.

Utilities of an ontology-based data model in music applications

Bridging the semantic gap, integrating computational tools and frameworks, and strongerfocus on the user can be cited among the most important future challenges of music infor-mation research (Casey et al., 2008). Prevailing machine learning tools based on statisticalmodels25 provide good solutions to particular problems in MIR, however they give little in-sight into our understanding of the musical phenomena they capture. In other words, theydo not easily allow us to close the semantic gap between features and computational modelson one hand, and musicological descriptors or human music perception on the other. Whilecognitive modelling, the use of contextual metadata in MIR algorithms, and the use of high-level reasoning are promising future directions; for a recent example see (Wang, Chen, Hu, &Feng, 2010), some common agreement in how knowledge and information are represented indi"erent systems is requisite for building on previous work by other researchers, or deployingcomplex systems. For example, the use of various socio-cultural factors such as geographicallocation, cultural background, gender, faith, political or sexual orientation in problems likemusic recommendation, artist similarity, popularity measure (hotness) or playlist generationis important in order to answer problems where solutions based solely on editorial metadata,the use of social tags or content-based features are insu!cient26.

Collins (Collins, 2010) presents a musicological study where influences on compositionare discovered through the use of the types of socio-cultural information mentioned above,combined with content-based audio similarity. While using traditional techniques such as Webscraping27, proprietary APIs of online sources like Last.fm28 or EchoNest29 and content basedfeature extractor tools such as Marsyas30 (Tzanetakis & Cook, 2000) or jAudio31 (McEnnis,McKay, Fujinaga, & Depalle, 2005) is not unfeasible in performing such studies, buildingrobust MIR systems based on these principles could be made easier through agreement on

24Commonly used RDF stores include Openlink’s Virtuoso, Garlik’s 4Store, Joseki or the D2R Server.25e.g. Support Vector Machines, Gaussian Mixture Models, Hidden Markov Models, or Bayesian Networks.26For more details, see sections on personal collection management and music recommendation in this paper.27Extracting information from Web pages using for example natural language processing.28Last.fm API: http://www.last.fm/api29The EchoNest: http://echonest.com/30Marsyas: http://marsyas.info/31jAudio: http://jmir.sourceforge.net/jAudio.html


how diverse music related data is represented and communicated. As a result of such anagreement, the laborious process of aggregating information would be reduced to makingsimple queries to a widely distributed resource of linked-data on the Semantic Web.

The integration of computational tools through the use of interoperable software compo-nents and extensible metadata management was the main focus of the OMRAS2 project. Wedescribe this framework through examples, and show how it supports reproducible researchas well as combining di"erent music analysis tools and data resources.

Finally, the collection and management of metadata in music production tools can be men-tioned in the context of user focus or enhanced usability, and as another use case of the MusicOntology. Primary reasons for collecting metadata in music production include the e"ectiveorganisation of musical assets, such as sounds in a sample library, or previously recorded takesof multitrack master recordings. There are several existing metadata standards and formatswhich could be used in these applications. MPEG-732, SDIF33, ACE XML34 (McKay, Bur-goyne, Thompson, & Fujinaga, 2009) and AAF35 are prominent examples. These existingformats however were designed for di"erent purposes covering overlapping musical domains,without a common higher-level metadata model. This seriously impairs the exploitation ofmetadata in ubiquitous creative applications for music production, as none of the standardscover editorial data, workflow data and content description in a uniform and extensible frame-work. For a more detailed analysis see (Fazekas & Sandler, 2009). A part of the problemcan be identified in the use of non-normative development and publishing techniques whencreating new metadata formats, rather than flaws in design. For example, using XML tostandardise the syntax does not provide su!cient grounds for interoperability between mu-sical applications or research tools, as further adaptation to the specific data model used ineach application is required.

The Music Ontology Framework

The power of RDF lies in the simplicity of its underlying data model; the decompositionof knowledge into statements consisting of subject, predicate and object terms. However,the components of these statements, (resources and literals naming concepts or relationships)may be selected in an ad-hoc manner. In order for our data to be meaningful for others,and to avoid ambiguities, we need to be able to define and later refer to concepts such as asong, a composer, or an audio processing plugin and its parameters. We also have to specifyrelationships, such as the association of a musical piece with a composer, pertinent in ourapplication. Creating an extensible ontological framework is among the first steps requiredfor producing interoperable research data sets and algorithmic components (for example, tosupport reproducible research), as well as for interlinking existing musical data on the Web.The Music Ontology serves this purpose (Raimond et al., 2007).

From this perspective, there are two important aspects of this ontology. The main partscan be used to describe the music production workflow in a broad sense—that is, the pro-cess of composition, performance and the creation of a particular recording. The secondimportant aspect is its extensibility. Several additions were produced in OMRAS2 includingsub-ontologies for describing music similarity, audio analysis algorithms and their output fea-tures, and musicological data such as chord progressions, keys or musical temperament. In

32Overview of MPEG-7: http://mpeg.chiariglione.org/standards/mpeg-7/mpeg-7.htm33Sound Description Interchange Format: http://sdif.sourceforge.net/34ACE XML: http://jmir.sourceforge.net/index ACE XML.html35Advanced Authoring Format: http://www.amwa.tv/publications.shtml


this section we briefly review the Music Ontology and some of its extensions relevant to theMIR community. The formal specification of the ontology is available online.36

Core Elements of the Music Ontology

The Music Ontology is built on several small ontologies specific to well-bounded domains.This includes the Timeline Ontology37, the Event Ontology38, the Functional Requirementsfor Bibliographic Records (FRBR) Ontology39, and the Friend Of A Friend (FOAF) Ontol-ogy40.

The Timeline Ontology is used to express instants and intervals on multiple concurrenttimelines, for example, backing an audio signal, a performance, or a musical score. The EventOntology is used to classify space–time regions. Using both the Event and the Timelineontologies, we are able to express information such as ‘at that particular time, the piano playerwas playing that particular chord’. The FRBR Ontology defines a layering in abstraction fromintellectual work to its physical manifestation. Finally, the FOAF Ontology covers people,groups and organisations.

The Music Ontology subsumes these ontologies with music-specific terms. These termscan be used for a wide-range of use-cases, from expressing simple editorial data such as tracksand album releases to the description of complex music creation workflows, from compositionto the recording of a particular performance of a work. On the highest level, it also providesfor the detailed description of complex events. However, the Music Ontology does not covereverything we can say about music, rather, it provides extension points on which more specificdomain models can be plugged. It provides a framework for describing audio signals andtemporal segmentations for example, but more specific ontologies extend it to describe audioanalysis results in detail. In the following, we give an account on extensions most relevant incommon MIR use cases and applications.

Extensions to the Music Ontology Framework

Chords and Musicological Features. A web ontology for expressing musical chords can beuseful in many applications, for example, finding specific chord progressions in a music libraryor building an online chord recognition system used in conjunction with the chord symbolservice described in this section. First however, we describe the chord ontology41. A briefoutline of other extensions for publishing musicological features such as tonality or musicaltemperament is provided last.

The chord ontology is grounded on the symbolic representation of musical chords describedby Harte et al. (Harte, Sandler, Abdallah, & Gomez, 2005). A chord is defined, in themost general case, by a root note and some constituent intervals. A chord inversion may beindicated by specifying the interval from the root to the bass pitch class. The ontology alsodefines a way to build chords on top of other chords. The concepts and relationships in thisontology are depicted in Figure 2 (Raimond, 2008). For example, we can use this ontology torepresent a D sharp minor with added ninth and missing minor third with the fifth being the

36The Music Ontology Specification: http://musicontology.com/37The Timeline Ontology Specification: http://purl.org/NET/c4dm/timeline.owl38The Event Ontology Specification: http://purl.org/NET/c4dm/event.owl39The FRBR Ontology Specification: http://vocab.org/frbr/core40The FOAF Ontology Specification: http://xmlns.com/foaf/spec/41The Chord Ontology Specification: http://purl.org/ontology/chord/


bass note. This chord is depicted in Figure 3 using standard musical score, while Figure 4shows its graph representation using the chord ontology.

Figure 2. Depiction of the main concepts and relationships in the Chord Ontology

Figure 3. D sharp minor over the fifth with added ninth and missing minor third.

We designed a namespace able to provide RDF representations using this ontology forchords expressed within Harte’s notation. The example depicted in Figure 4 can be ac-cessed at the URI http://purl.org/ontology/chord/symbol/Ds:min7(*b3,9)/5, whichitself corresponds to the chord symbol Ds:min7(*b3,9)/5 in Harte’s shorthand notation.

In this notation, the root note is written first followed by a colon and a shorthand forchords common in western music. This can be seen as a label associated with pre-set chordcomponents as defined in Harte’s paper (Harte et al., 2005). Extra or missing intervals canbe contained by parentheses with missing degrees (that would normally be present in a minorchord for example) denoted using an asterisk. The bass note may optionally be specified aftera forward slash. The ontology also defines a chord event concept subsuming the event conceptin the Event ontology described in the previous section. We may use these events to classifytemporal regions corresponding to particular chords.

Besides describing chords, the full framework of ontologies also provides for expressingtonality42, symbolic music notation43 and the particularities of instrument tuning44 used

42The Tonality Ontology: http://motools.sourceforge.net/doc/tonality.html43The Symbolic Music Ontology: http://purl.org/ontology/symbolic-music/44The Temperament Ontology: http://purl.org/ontology/temperament/


Figure 4. Representation of a D sharp minor over the fifth with added ninth and missing third.Shaded parts of the graph correspond to RDF statements that may be accessed using the correspondingweb identifiers.

in a recording. Additional ontologies describing content-based features and audio analysisalgorithms are described next.


Content-based Audio Features. The Audio Features ontology45 (AF) can be used to expressboth acoustical and musicological data. It allows for publishing content-derived informationabout musical recordings. This information can be used to share data sets such as theones described in (Mauch et al., 2009), or interoperate software components such as SonicVisualiser46 and Sonic Annotator47.

The ontology provides concepts such as Segment or Beat. For instance, it can be usedto describe a sequence of key changes corresponding to key segments in an audio file. Thethree main types of features we may express are time instances such as onsets, time inter-vals for instance the temporal extent of an audio segment with a specific musical key, andfinally dense features which can be interpreted as signals themselves such as spectrogramsand chromagrams. The relationships between core concepts of this ontology is summarisedin figure 5.

We may describe a note onset as an example of using the AF ontology. This is shownin the RDF snippet of listing 3. Here, the first statement declares a timeline which can beused to represent the temporal extent of an audio signal. We simply state that the resourcesignal timeline is an instance of the tl:Timeline class.48 Note that in the Turtle syntaxkeyword a is used as a shorthand for the rdf:type predicate.

Figure 5. Core Audio Features Ontology

Next, we declare an onset resource, an instance of af:Onset. Using the event:time predicatewe associate it with a particular time instant placed on the signal timeline. The square bracket

45The Audio Features Ontology: http://motools.sourceforge.net/doc/audio features.html46Sonic Visualiser is a program to display content-based audio features.47Sonic Annotator is a batch feature extractor tool. We provide detailed descriptions in Applications.48The association of this timeline with an actual signal resource is omitted for brevity.


:signal_timeline a tl:Timeline .

:onset_23 a af:Onset ;

event:time [

a tl:Instant ;

tl:timeline :signal_timeline ;

tl:at "PT1.710S"^^xsd:duration ;

] .

Listing 3 Describing a note onset relative to the signal timeline.

notation represents a blank or unnamed resource which is used here for convenience. Thegroup of statements in brackets declare the class membership of this instance, associate itwith the signal timeline and state its exact position relative to the beginning of the signalusing the XML Schema Duration datatype.49 Note that onsets are modelled as instantaneousevents for simplicity. See (Bello et al., 2005) for the interpretation of onset times. The AudioFeatures ontology presently has no exhaustive coverage of all possible features we may wish toexpress, however, it subsumes basic concepts from the Timeline and Event ontologies. If weneed to publish features that have no predefined terms in the AF ontology, we can synthesisea new class within an RDF document as a subclass of an appropriate term in the lower levelontologies mentioned above. This ensures that our features can be interpreted correctly astime-based events, even if specific semantic associations are not yet available.

Audio Signal Processing. While the Audio Features ontology can be used to publish bothsemantic and signal processing features50 extracted from audio content, it does not providefor the description of algorithms that were used to produce these results in the first place.We believe that this kind of provenance information is equally valuable, and, when it comesto sharing research data, it is inevitable if we are to make meaningful comparisons. Whereasthe ontology presented here is specific to our Vamp API format (Cannam, 2009), it providesan example of linking algorithms, parameters and results in a traceable manner51.

Vamp plugins52 are a set of audio analysis plugins using a dedicated API developed in theOMRAS2 project. The corresponding Vamp Plugin Ontology53 is used to express informationabout feature extraction algorithms wrapped as Vamp plugins. The most useful aspect of thisontology is the association between plugin outputs and Audio Features ontology terms in orderto describe what they return. These may be distinct event types like note onsets, featuresdescribing aspects of the whole recording such as an audio fingerprint, or dense signal datasuch as a chromagram.

Besides describing analysis results, it is important to denote specific parameters of audioanalysis algorithms. The Vamp Transform Ontology accommodates this need. Figure 6 showsits basic, open-ended model.

49See http://www.w3.org/TR/xmlschema-2/ for details on built-in XML Schema datatypes.50The output of DSP algorithms that produce representations of a music signal that have no corresponding

interpretation in the music domain such as Cepstral or Wavelet coe!cients.51For a concrete application please see the description of SAWA in the section about Automated Audio

Analysis on the Web.52Vamp plugins: http://vamp-plugins.org/download.html53The Vamp Plugin and Transform Ontologies: http://omras2.org/VampOntology


Figure 6. The Vamp Transform Ontology

This ontology, although it is conceptually separate, was published together with the VampPlugin ontology. It contains terms to describe how a plugin may be configured and run. Itcan be used to identify parameter values and other details such as audio block and step sizes.This information is expressed and stored using the same RDF format as the results withoutimposing any additional encoding requirements. Therefore, an agent reading these results willhave certainty about how they were generated (Fazekas, Cannam, & Sandler, 2009). Thisvery valuable detail ensures reproducible experiments, which is a central concern of MIR andof all scientific research.

Music Similarity. Music similarity is an important concept for music recommendationand collection navigation. It is assumed that if a user expresses interest in a particular pieceof music, that user is also interested in other pieces of music that are similar in some sense.For example, songs exhibiting acoustical features that are close according to some distancemeasure (content-based similarity54), or songs composed or performed by artists who havecollaborated in the past, or who were born in the same city (cultural similarity). The use ofthe latter type of similarity is well exemplified by the CatfishSmooth web application55.

Level one of The Music Ontology provides a very basic mechanism for dealing with musicsimilarity in the form of the mo:similar to property. For example we may apply this propertyto an instance of mo:MusicArtist to point to another mo:MusicArtist.

:Chopin a mo:MusicArtist .

:Debussy a mo:MusicArtist ;

mo:similar_to :Chopin .

Listing 4 Expressing simple similarity between two composers.

Unfortunately, we have no information about how these artists are similar or who isclaiming they are similar. We could apply a Named Graphs approach (Carroll, Bizer, Hayes,

54See for example SoundBite or SAWA-recommender in section Recommendation, Retrieval and Visualisationby Similarity

55available at: http://catfishsmooth.net/


& Stickler, 2005) to this similarity statement. By treating the last triple in the statementabove as a Named Graph we can attach additional properties to that statement. However,this can lead to a clumsy syntax and a data set that is not optimized to work with most triplestore implementations.

Alternatively we can use the Similarity Ontology56 to make similarity statements withtransparency and provenance. In the Similarity Ontology we treat similarity as a class ratherthan a property. We define instances of sim:Similarity, a sub-class of the the broaderconcept sim:Association, to make similarity statements. The previous example could bere-written using the Similarity Ontology as shown in listing 5.


:Debussy a mo:MusicArtist .

:ComposerSim a sim:Similarity ;

sim:element :Debussy ;

sim:element :Chopin .

Listing 5 Similarity as a reified concept.

Here we see that :ComposerSim is an instance of sim:Similarity. We use the sim:elementproperty to specify the music artists involved in this similarity statement, but we have notprovided any additional information about them. We can describe in what way these musicartists are similar by introducing an instance of sim:AssociationMethod:


:Debussy a mo:MusicArtist .

:CharlesSmith a foaf:Person .

:ComposerInfluence a sim:Similarity ;

sim:subject :Chopin ;

sim:object :Debussy ;

sim:method :InfluenceMethod .

:InfluenceMethod a sim:AssociationMethod ;

foaf:maker :CharlesSmith ;

dc:description "Similarity by composer influence" .

Listing 6 Describing similarity using di"erent levels of expressivity.

We further reify57 the sim:AssociationMethod by specifying who created this methodand providing a brief textual description. Also note we use sim:subject and sim:object

instead of sim:element to specify this is a directed similarity statement, in this case per-taining to influence. We can include a variety of sim:AssociationMethods in the samedataset to allow for multi-faceted similarity - two items can be similar in more than one

56the Similarity Ontology is sometimes referred to as MuSim57Reification is the process of representing a relation with an object so that you can reason about the

relation. This may be achieved for example by treating an RDF statement as a resource in order to makefurther assertions on its validity, context or provenance.


sense. Furthermore, using the Similarity Ontology enables an intuitive and extensible methodfor querying similarity using the SPARQL query language. For example, in addition tospecifying which artist we want to include in our similarity query, we can specify whichsim:AssociationMethods we want to include as well. In this way, we can select only thesimilarity statements that are appropriate for our application. Using the same mechanism ina distributed environment where similarity statements might be published by a heterogeneousset of agents, we can choose to select only similarity statements created by agents we trust.

Additional details about the Similarity Ontology, including information on the disclosureof similarity derivation methods can be found in (Jacobson, Raimond, & Sandler, 2009)

Data Sets

Within the scope of OMRAS2 and the Linking Open Data project, we published and in-terlinked several music-related datasets using the ontologies described in the previous section,on our DBTune server58. The di"erent published datasets are described in table 1. Thesedatasets are available as Linked Data and through SPARQL end-points. When possible, RDFdumps were also made available.

Dataset Millions of triples Interlinked with

http://dbtune.org/magnatune/ 0.322 DBpediahttp://dbtune.org/jamendo/ 1.1 Geonames, Musicbrainzhttp://dbtune.org/bbc/peel/ 0.277 DBpediahttp://dbtune.org/last-fm/ ! 600 Musicbrainzhttp://dbtune.org/myspace/ ! 12,000http://dbtune.org/musicbrainz/ ! 60 DBpedia, Myspace, Lingvojhttp://dbtune.org/iso/ 0.457 Musicbrainz

Table 1: Linked datasets published within the scope of the OMRAS2 project. When the dataset isdynamically generated from another data source (e.g. Myspace pages), its size is approximate.

The Jamendo and Magnatune end-points hold data published on the websites of the re-spective labels. The BBC end-point holds metadata released about the John Peel sessions.The Last.fm service provides live RDF representation of tracks submitted to Last.fm usingan AudioScrobbler59 client. The MySpace service provides URIs and associated RDF rep-resentations of top-friends and available tracks on a given MySpace page. The MusicBrainzservice can be used to access the MusicBrainz database as a Linked Data resource, while theIsophone dataset holds content-based music similarity features described in the next section(see Recommendation, Retrieval and Visualisation by Similarity).

Additional datasets which are available as RDF documents also include chord transcrip-tions from the Real Book60, and various music annotations described in (Mauch et al., 2009).Extending this set of ground truth annotations and making them available via a SPARQLend-point constitutes future work.

58see http://dbtune.org/59http://www.audioscrobbler.net/ see also: http://www.last.fm/api60http://omras2.org/ChordTranscriptions


Applications

There are two types of applications we can build using the previously described technolo-gies and data sets. Applications that directly contribute to the Semantic Web may answercomplex queries by aggregating data on demand, provide inferencing services, or, in case ofmusic processing, drive a signal analysis engine. These applications are commonly accessiblevia SPARQL end-points. Another kind of application, directed at the end-user, may useSemantic Web resources and technologies to enhance the user experience. In this section weprovide examples of both types of applications created within the OMRAS2 project.

Musicological Analysis and Music Information Research

Several fields of research may benefit from the enhanced interoperability and usability pro-vided by Semantic Web technologies. Musicological studies using large audio collections anddatabases is one of them. While the use of automatic audio analysis and the exchange of re-search data-sets is more and more common between musicologists, these data are commonlyrepresented in arbitrary formats. This makes the interaction between researchers di!cult.The solution to this problem is to represent this data in a structured and open-ended frame-work. To this end, RDF and the Music Ontology provides good grounds, but in order tomake them easy to use we also need software tools which understand these formats. TheOMRAS2 project produced numerous applications which can be used in music informationscience or musicological work. The two most prominent ones are Sonic Visualiser61 and SonicAnnotator62.

Sonic Visualiser is an audio analysis application and Vamp plugin host. It can also beused for viewing features calculated by other programs by loading a set of features associatedwith an audio file from an RDF document. Sonic Annotator can be used to extract content-based features from large audio collections. It applies Vamp feature extraction plugins toaudio data, and supports feature extraction specifications in RDF using the Vamp TransformOntology. Users of these applications benefit from the uniform representation of features usingthe Audio Features Ontology, and the ability of interlinking these features with contextualdata available on the Semantic Web.

Automated Audio Analysis on the Web

Extracting semantic descriptors directly from audio recordings can be useful in audiocollection management and content-based music recommendation. Examples of these de-scriptors are onset times, beats, chord progressions and musical keys. These data become avaluable resource when interlinked with cultural or editorial metadata expressed within thesame ontological framework. Two applications were developed in OMRAS2 for such tasks.Henry63 (Raimond, Sutton, & Sandler, 2008) can be used to provide an on-demand musicanalysis service wrapping audio analysis algorithms implemented as Vamp plugins. For ex-ample, the SPARQL query shown in listing 7 triggers the key detection algorithm describedin (Noland & Sandler, 2007).

Henry recognises built-in predicates such as mo:encodes, vamp:qm-keydetector orvamp:qm-chromagram. These predicates are evaluated at query time, and the appropriate

61Available from: http://sonicvisualiser.org/62Sonic Annotator is available from http://omras2.org/SonicAnnotator63See http://code.google.com/p/km-rdf/wiki/Henry for details on Henry.


SELECT ?sig ?result

WHERE {

<http://dbtune.org/audio/Den-Nostalia.ogg> mo:encodes ?sig .

?sig vamp:qm-keydetector ?result }

Listing 7 A SPARQL query for extracting musical key.

signal processing algorithms are invoked. For instance, mo:encodes will result in decodingthe audio file, while vamp:qm-keydetector will trigger the key detection algorithm. Henrycaches the results of previous computations, and may also be used to link these results withcontextual data.

While Henry can be accessed via a SPARQL endpoint, SAWA64 (Sonic Annotator WebApplication) (Fazekas et al., 2009) focuses on providing a user-friendly65 interface. It em-ploys Semantic Web technologies under the hood to o"er services to the end-user and audioresearcher. SAWA’s Web-based user interface can be seen in figure 7. It allows batch featureextraction from a small collection of uploaded audio files. The interface provides for the se-lection and configuration of one or more Vamp plugin outputs, and execute transforms66 ofpreviously uploaded files. The results are returned as RDF documents. This can be exam-ined using an RDF browser such as Tabulator, or imported in Sonic Visualiser and viewed incontext of the audio.

Uniform representation and the linked data concept are key in creating modular archi-tectures. For instance, SAWA’s server side application uses RDF to communicate with othercomponents such as Sonic Annotator, its computation engine; and interprets RDF data togenerate its user interface dynamically. The advantages of using this format are manifold.The interface can be generated for any number of plugins given that their descriptions areprovided according to Vamp plugin and transform ontologies. Cached results may be returnedfrom a suitable RDF store instead of repeating computation. Finally, these systems can accessother linked data services and augment the results with di"erent types of metadata.

The main intent behind SAWA is to promote the use of RDF and the Music Ontologyframework in the MIR community. The audio analysis capabilities of both SAWA and Henryare similar, albeit narrower in scope when compared to the EchoNest web service67; mentionedpreviously, however, the results are expressed in more general and extensible RDF withoutthe need for a proprietary XML-based return format. Further details on the components ofthe SAWA system are available in (Fazekas et al., 2009).

Personal Collection Management

Much previous research in the MIR community have been concerned with personal col-lection management tasks including automatic playlist generation, as well as finding intuitivenew ways of navigating through the ever increasing music collection on the average personal

64SAWA is available at http://www.isophonics.net/sawa/65The present version can be accessed using a web-based graphical user interface, while a SPARQL end-point

for accessing accumulated audio features is under development.66A transform is seen here as an algorithm associated with a specific set of parameters.67The EchoNest also provides audio analysis services, however its functionality extends beyond content based

feature extraction.


Figure 7. Dynamically generated Web interface for a set of audio analysis plugins and parameterconfiguration of a note onset detector plugin.

computer. Early attempts however were limited to the use of metadata available about eachsong in a collection at a particular place and time (Pauws & Eggen, 2003). Recognising theinadequacy of editorial metadata in some cases, other researchers were focusing on the use ofcontent-based features only (Rauber, Pampalk, & Merkl, 2003). Some common conclusionscan already be drawn from these results; no single source of metadata is adequate in alllistening situations, and locally available metadata tags such as genre (or even the name ofthe artist in an mp3 file) are not reliable (Pauws & Eggen, 2003). Several applications werebuilt in OMRAS2 which demonstrate the use of interlinked music-related data sources andmitigate the problems mentioned above.

We developed two tools to aggregate Semantic Web data describing arbitrary personalmusic collections. GNAT68 finds, for all tracks available in a collection, the correspondingweb identifiers in the Semantic Web publication of the Musicbrainz dataset mentioned earlier.GNAT uses primarily a metadata-based interlinking algorithm described in (Raimond et al.,2008), which was also used to interlink the above described music-related datasets. GNARQLcrawls the Web from these identifiers and aggregate structured information about them com-ing from heterogeneous data sources. GNAT and GNARQL then automatically create atailored database, describing di"erent aspects of a personal music collection. GNARQL pro-vides a SPARQL end-point, allowing this aggregation of Semantic Web data to be queried.For example, queries such as “Create a playlist of performances of works by French com-

68GNAT and GNARQL are available at http://sourceforge.net/projects/motools.


posers, written between 1800 and 1850” or “Sort European hip-hop artists in my collectionby murder rates in their city” can be answered using this end-point. GNARQL also gives afaceted browsing interface based on /facet (Hildebrand, Ossenbruggen, & Hardman, 2006),as illustrated in Figure 8.

Figure 8. Management of personal music collections using GNAT and GNARQL. Here, we plot ourcollection on a map and display a particular artist.

Recommendation, Retrieval and Visualisation by Similarity

The aim of music recommendation is the suggestion of similar artists, tracks or eventsgiven, for instance, a seed artist or track. While collaborative filtering69 is predominant inthis field, there is a growing trend towards incorporating more diverse data in recommendersystems. Using collaborative filtering alone exhibits a number of problems such as cold start(lack of initial ratings), and has poor e!ciency in discovering the long tail of music produc-tion (Celma & Cano, 2008).

The inclusion of diverse cultural and contextual data or content-based similarity may bebeneficial when tackling these problems. However, obtaining these data requires expensiveand often unreliable text mining procedures (Baumann & Hummel, 2005) or the adaptationto proprietary Web APIs. On the other hand, the use of Linked Data can save both thecomputation steps (such as part-of-speech tagging) needed to extract meaning from unstruc-tured Web documents, or writing the necessary glue code between a particular applicationand di"erent Web-based data sources.

69These systems make recommendations based on behaviour or preferences of listeners within a large usercommunity. (See http://www.last.fm/ for an example).


Using Linked Data in this fashion is beneficial when generating music recommendationsderived from a number of interlinked data sources (Raimond, 2008; Passant & Raimond,2008). The set of RDF statements linking the seed resource and the recommended resourcecan be used to generate an explanation of the corresponding recommendation (e.g. “this artistplayed with that other artist you like, the 4th of June 1976 in Manchester”).

Structured and linked musical data may also be useful in intuitive visualisation andfaceted browsing of music collections, artist networks, etc. The k-pie visualisation technique(Jacobson & Sandler, 2009) can be used to browse a large network of artists by similarityderived from social networks, for example, MySpace. Another visualisation tool, the ClassicalMusic Universe70 is used to discover influences between classical composers. The tool usesdata from the Semantic Web to build a network of composers. This data is mashed up withmore information from DBpedia and DBtune. Most recently the CatfishSmooth website71

allows users to follow connections between music artists by leveraging linked data. A varietyof data sources are used - including MusicBrainz, Last.fm, DBpedia and others - to find mu-sic related connections (e.g. artists that have collaborated) and connections that are moretangential to music (e.g. artists that have converted to the faith of Islam).

The use of content-based similarity features can be valuable in the development of musicrecommendation and retrieval systems. However, the lack of large open music databases is acommon problem for the researcher. For this reason, a data collection tool has been devel-oped in OMRAS2 as part of SoundBite72, an automatic playlist generator (Levy & Sandler,2006). This tool is available as a plugin for iTunes and SongBird. SoundBite collects acousticsimilarity features based on MFCC (Mel-Frequency Cepstral Coe!cients) distributions. Dataof about 150,000 tracks aggregated over a 2 year period have been cleaned73 and publishedon the Semantic Web as described in (Tidhar, Fazekas, Kolozali, & Sandler, 2009). We builta content-based recommender to demonstrate the application of this dataset.

SAWA-recommender74 is a query by example music search application made available onthe Web. A query to this system may be formed by a small set of songs uploaded by the user.It is evaluated either by considering similarity to any of the uploaded songs (and ranking theresults appropriately), or a single common query is formed by jointly calculating the featuresof the query songs. The query is matched against the Isophone database holding similarityfeatures and MusicBrainz identifiers associated with each song. This allows access to editorialmetadata consisting of basic information such as song title, album title and the main artist’sname associated with each song in the results. Following links based on MusicBrainz identifiersthe user may be able to access more information, for instance, BBC Music75 artist pages.

Creative Record Production

A computer-based system may be seen as intelligent, if it accomplishes feats which requirea substantial amount of intelligence when carried out by humans (Truemper, 2004). In thecontext of creative music production, we may think of an audio application which assists thework of a recording engineer by providing intelligent tools for music processing and contentmanagement. Such a system has to aggregate and process diverse information from audio

70http://omras2.org/ClassicalMusicUniverse71http://catfishsmooth.net/72SoundBite is available from: http://www.isophonics.net/73Features from poorly identified or low quality audio sources were removed.74http://isophonics.net/sawa/rec75http://www.bbc.co.uk/music/


analysis and from user interaction. The information needs of audio production tools howeverare not easily planned, therefore we need an open-ended data model and software framework.For these reasons, Semantic Web technologies can pave the way towards building intelligentsemantic audio tools.

The first simple application of this is described in (Fazekas, Raimond, & Sandler, 2008).During a recording session, a wealth of information can be collected about music production.Based on this recognition, we designed a metadata editor which enables the user to describea recording project using Music Ontology terms. We also provide an ontology for linking thisdata to audio files in a multi-track environment76.

An extension to this system stores and processes all information including content-basedfeatures and user entered information arising in the recording environment. Using the Seman-tic Desktop paradigm (Decker & Frank, 2004), the idea of an intelligent desktop environment,this system may be seen as the Semantic Audio Desktop. By definition, the Semantic Desk-top is a device in which an individual stores digital information (Sauermann, Bernardi, &Dengel, 2005) — that is, data which are otherwise stored on a personal computer by means ofconventional techniques such as binary files or spread sheets. Its most prominent aim is theadvancement of personal information management through the application of Semantic Webtechnologies to the desktop environment. If we extend this idea to the audio production envi-ronment, and in particular, audio editors used in post production, we shall be able to collecthigh-quality metadata during the production process and open up the creative environmentto social media77. The metadata — being in the same format as other data on the SemanticWeb — can be fed back into the public domain in a highly useful way; these data may be usedin content-based music recommendation and search services, advanced cataloguing, as well asmusic education, collaborative music making and numerous future Web-based applications.In (Fazekas & Sandler, 2009), we describe a software library which enables the implementa-tion of this system in existing audio editors. Besides contributing music production data tothe Semantic Web, this may be seen as a basis for an intelligent audio editor.

Conclusions

In this article, we described how various areas of music information research can benefitfrom using the Web of data, a set of Web standards known as Semantic Web technologies,Web Ontologies and the Linked Data concept. We also noted how our field of research mayenrich the Semantic Web and reviewed the contributions of OMRAS2 to both areas.

Sharing data, for example, automatic annotations of music recordings, as well as enablingthe use of diverse cultural, editorial and content-based metadata are among the main objec-tives of the project. For these reasons, an ontological framework was developed in OMRAS2for publishing music related information. The Music Ontology can be used to describe almostevery aspect of music in detail. While it is primarily aimed at the music information commu-nity and application developers using the Semantic Web, it can also be applied in most of thearchival, librarian and creative use cases described in (Canazza & Dattolo, 2009) with a morestraight-forward data model and lots of already available standard tools for processing RDFdata. We also feel important to note that the Music Ontology has become a de-facto standardfor representing music related information on the Semantic Web as opposed to more general,

76The Multitrack Ontology http://purl.org/ontology/studio/multitrack77Internet-based applications that build on the ideological and technological foundations of Web 2.0, and

that allow the creation and exchange of user-generated content. (Kaplan & Haenlein, 2009)


but also more complicated multimedia ontologies such as COMM78 (Arndt, Troncy, Staab,Hardman, & Vacura, 2007) with some overlap in the music domain. It can equally be used bymusic researchers to exchange data, or Semantic Web developers to create applications usingdisparate musical resources. Therefore, it is a major contribution to both the MIR and theSemantic Web communities.

A large number of data sets have already been published and interlinked using the MusicOntology. However, this is an ongoing work in the project and also an important part of ourfuture work. We demonstrated several uses of Linked Data, and showed how the researchcommunity may benefit from using these data sources, and how everyone might benefit frompublishing music related data as Linked Data. This includes the ability of creating intuitivevisualisations or mash-ups such as the Classical Music Universe and GNARQL. We alsoshowed how MIR research can contribute to the data Web by providing automatic musicanalysis services such as Henry, and Web applications like SAWA. This demonstrates howthe Resource Description Framework and various extensions to the Music Ontology can beadapted to di"erent purposes. Finally, we explored how these technologies can serve the needsof intelligent music processing systems.

Acknowledgements

The authors wish to thank to Simon Dixon and the anonymous reviewers for their valuablecomments which helped us in improving this article. We acknowledge the support of theSchool of Electronic Engineering and Computer Science, Queen Mary University of London,and the EPSRC-funded ICT project OMRAS- 2 (EP/E017614/1).

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Documents

An overview of Semantic Web activities in the OMRAS2 projectgyorgyf/files/papers/Fazekas2010jnmr.pdf · project † Gy¨orgy Fazekas ... (Musicbrainz, Magnatune14 and Ja-mendo15)