The Linking Open Data cloud contains several music related datasets that hold great potential for enhancing the process of research in the eld of Music Information Retrieval (MIR) and which, in turn, can be enriched by MIR results. We demonstrate a system with several related aims: to enable MIR researchers to utilise these datasets through incorporation in their research systems and workows; to publish MIR research output on the Semantic Web linked to existing datasets (thereby also increasing the size and applicability of the datasets for use in MIR); and to present MIR research output, with cross-referencing to other linked data sources, for manipulation and evaluation by researchers and re-use within the wider Semantic Web. By way of example we gather and publish RDF describing signal collections derived from the country of an artist. Genre analysis over these collections and integration of collection and result metadata enables us to ask: "how country is my country?".
Much of the work of researchers in the eld of Music Information Retrieval (MIR) focusses on the algorithmic extraction of information from music. However, there are many problems associated with the design and implementation of distributed systems within which such algorithms might be deployed.
We can broadly describe the process an MIR researcher typically follows in
three steps; we also highlight some of the issues and, at an abstract level, how
linked data and semantic web technologies might assist in building a complete
system.
1. Assemble a collection of audio input. To evaluate an algorithm, the
researcher must acquire a wide selection of signal typically digital audio
les for the algorithm to process. Music recordings are often restricted
from free exchange amongst researchers, either explicitly through copyrights
or implicitly through the high overheads of managing detailed and
intricate licensing. Even when audio data is freely available and distributable
a dicult balance must be found to avoid over-tting of algorithms to a
particular set of signals: whilst a widely shared, understood, and re-usable
collection is critical for comparative evaluation, tuning an algorithm to such
a collection during development (knowing it will be the benchmark) is likely
to detrimentally aect performance against more randomly selected input
(i.e. real-world tests). It is therefore useful to create and modify large
collections of audio data quickly and exibly which can be shared between
researchers for comparative evaluation. Restrictions on the distribution of
actual audio les can be accommodated through the separate description of
collections and correctly modelling the relationship between artefacts (e.g.
distinguishing between a work, a performances of the work, recordings of the
performance, and published media of the recording); metadata exchange can
then occur independently and be cross-referenced against any institutional
or other private archive of audio. Linking existing metadata for audio les
and basing collection generation on this information is desirable for quickly
trialling an algorithm against particular musical facets (e.g. a particular
period and style derived from the composers).
2. Apply the algorithm to the audio input. There are many MIR systems
which enable an algorithm to be applied to signal. More recently some
systems have begun to adopt practices and tools from the scientic workow
community, for example the Meandre workow enactment system [
Employing new RDF encodings for collections and results that utilise existing
ontologies (including the Music Ontology, GeoNames, Provenance Vocabulary,
and OAI-ORE), and by deploying a linked data audio le repository and services
for publishing collections and results, we present a proof-of-concept system that
addresses the problems outlined in the previous section. While the principles
and design described here can be applied to all MIR systems, for demonstration
purposes we have developed a specic use case known as Country/country. In
this section we outline the components of the system, which approximately align
to the steps in the previous section (with the addition of a pre-step), detailing
the generic purpose of each service, followed by the specic implementation in
Country/country ( in italics ).
0. An Audio File Repository which serves audio les and linked data about
the audio les using HTTP. For our public demonstrator a subset copy
of the free-licensed Jamendo collection 1 has been used. Using the Music
Ontology[
Within the (Meandre-based) genre classication workow a head-end component has been written to dereference each track URI passed to the workow and, using the linked data published by the signal repository, retrieve both the local copy of the audio le and the reference to the original Jamendo identier . This URI persists through the genre analysis workow until it reaches a new tail-end component where the analysis is published using RDF including links back to the Jamendo URI. 1 http://www.jamendo.com/ 2 http://dbtune.org/jamendo/ 3 http://www.geonames.org/ontology/ 3. A Results Viewer web application retrieves the collections RDF from 1) and results RDF from 2), cross-referencing them via the URIs used throughout the system. The user can identify trends in genre classication within and between collections. Results can be pooled and compared using existing and new collections and inform the creation of new sets. To demonstrate how further links can easily be made to existing datasets and inform derivative collection generation, relevant associations from other linked data sets are shown (e.g. artists of the same genre and country from DBpedia and the BBC for a particular analysed track ). 3
The Country/country demonstrator system is available at: http://www.nema.ecs.soton.ac.uk/countrycountry/
Many thanks to Stephen Downie and his team at IMIRSEL University of Illinois for access and source code to their Son of Blinkie genre classication workow and the myExperiment team at the University of Southampton for help and guidance while developing the Meandre extension. This work was carried out through the Networked Environment for Musical Analysis (NEMA) project funded by the Andrew W. Mellon Foundation, and the Structural Analysis of Large Amounts of Musical Information (SALAMI) project funded by the JISC Digitisation and e-Content programme as a part of the Digging into Data challenge.