Due to the evolution of description formats for music content and context data, there are presently manifold specifications available for representing various musical characteristics to a different complexity. A main division can be made regarding their bondage to audio signal file formats [Gän09a]. On the one side, there are format-bound specifications, e.g., the well-known ID3 tags [O’N10] or Vorbis Comments [sev11j]. These are predefined data containers that are part of the audio or multimedia files of the respective file formats, e.g., MP3 [Nil00] or OGG [GPM08]. On the other side, there are format-independent specifications, e.g., the Music Ontology framework (see below) or MPEG-7 Audio [MSS03]. They usually make use of different data models, e.g., semantic graphs, and description formats, e.g., RDF or XML, for the purpose of defining spec- ifications and for representing instantiations. Music metadata formats of MMSs also belong to this category (see Subsection 2.3.3). Nevertheless, they are often based on proprietary database schemata, e.g., the Next Generation Schema (NGS) of MusicBrainz (see Section 2.3.3.1). For that matter, explicit mappings are always required in a information federation KMS to process in- formation resources that are originally represented with the help of such varying KR vocabularies. Corthaut et al. did an analysis of various common MMFs [CGVD08] by comparing their appli- cability to a set of important music application domains, and their coverage regarding a set of universal clusters (semantically related concepts and relations). They concluded that there was not a single music metadata standard that fulfils all requirements of the different application do- mains at once. However, format-independent specifications in general attempt to compensate
music document
audio signal
signal sampling
analysis / texture windows
time-frequency representation
auditory system models
histograms
direct statistics
spectral statistics
perceptual statistics
musical
fingerprints
STFT, DWT, VRT, ... Mel scale, Bark scale rhythm, pitch, timbreFigure 2.4: The audio signal feature extraction and derivation process [Gän09b]
weaknesses of format-bound specifications to enhance interoperability [Gän09a]. One outcome of the MMF analysis that I undertook as a part of my Belegarbeit [Gän09a] and which can be seen as a succession of the research of Corthaut et al. [CGVD08] was, that it is important to utilise an open, variable, and extensible KR framework, such as Semantic Web ontologies. Such a set of KRLs is to be able to satisfy evolving requirements of different music application domains.
The Music Ontology Framework A framework of Semantic Web ontologies that establishes a good foundation for describing musical characteristics is the Music Ontology framework as it is introduced by Raimond et al. in [RASG07]. It is explained, compared and evaluated in detail in the dissertation of Yves Raimond [Rai08a]. Besides, the analysis of Corthaut et al. [CGVD08] and that one carried out in my Belegarbeit [Gän09a], both demonstrated the expressiveness and ap- plicability of the Music Ontology framework, especially regarding the requirements of describing personal music collections.
music document metadata
audio signal analysis
metadata enhancements feature vectors
(personal) music knowledge base
Aperture, Echo Nest, ...
Web Services, search engines,
music information web sites, SPARQL endpoints, local / private
networks, ... multivariate analysis, machine learing, ontologies triple stores, audioDB
classification, categorisation, similarities
This vocabulary framework consists of several Semantic Web ontologies that attempt to heav- ily involve existing KR definitions, e.g., the Friend of a Friend Vocabulary (see Section 2.4.1) or the Functional Requirements for Bibliographic Records Vocabulary (FRBR) [DN09]. Furthermore, it has a strong aspect on modularisation and reutilisation. For this reason, several simple spe- cialised ontologies24, which represent a concrete aspect, are an outcome of the development process of the Music Ontology framework. These are, for example, the Timeline Ontology (TL) [RA07b], which is designed on top of the OWL Time Ontology (TIME) [Hob06] to express a time- line concept, or the Event Ontology (EVENT) [RA07a] to be able to model events as first-class entities.
The core ontology of this framework, the Music Ontology (MO) [RGJ+10], amongst others, makes use of these simple specialised ontologies. This vocabulary is divided into three main levels, which are related to a different expressiveness. Starting from basic editorial metadata (e.g. music artist, track or label), over terms that can be used to describe a creation workflow (e.g. composition, performance, recording), to event decomposition (e.g. to describe the struc- ture of a song). Especially, the last level is mainly modelled in separate ontologies that are built on top of the core ontologies of the Music Ontology framework, e.g. the Audio Features Ontology (AF) [Rai08b].
Besides the MO specification documentation [RGJ+10], a community25 wiki [sev11e] provides further explanations, examples, etc. For instance, comprehensive illustrations of different con- ceptual parts of this vocabulary (see [RGG10]) or an exhaustive example, which contains descrip- tions starting from an abstract idea of a music album and its songs to concrete items of a release of this album that are owned by someone (see [Gän11c]), are available in the wiki. An overview of extension modules and related ontologies, e.g. the Similarity Ontology (SIM) [JRG10], can be found as well on this website (see [sev11c]).
The Music Ontology is one of the most popular Semantic Web ontologies (see, e.g., its usage on the Linked Open Data cloud [BJC11]). It is utilised in, e.g., BBC Music26, Libre.fm27 and MusicBrainz (via the LinkedBrainz project [DJ10]). Moreover, several SPARQL endpoints of proof- of-concept datasets are accessible on DBTune28 that demonstrate the manifold applicability of MO.