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dc.contributor.authorHargreaves, Steven
dc.identifier.citationHargreaves, S. 2014. Music Metadata Capture in the Studio from Audio and Symbolic Data. Queen Mary University of Londonen_US
dc.description.abstractMusic Information Retrieval (MIR) tasks, in the main, are concerned with the accurate generation of one of a number of different types of music metadata {beat onsets, or melody extraction, for example. Almost always, they operate on fully mixed digital audio recordings. Commonly, this means that a large amount of signal processing effort is directed towards the isolation, and then identification, of certain highly relevant aspects of the audio mix. In some cases, results of one MIR algorithm are useful, if not essential, to the operation of another { a chord detection algorithm for example, is highly dependent upon accurate pitch detection. Although not clearly defined in all cases, certain rules exist which we may take from music theory in order to assist the task { the particular note intervals which make up a specific chord, for example. On the question of generating accurate, low level music metadata (e.g. chromatic pitch and score onset time), a potentially huge advantage lies in the use of multitrack, rather than mixed, audio recordings, in which the separate instrument recordings may be analysed in isolation. Additionally, in MIR, as in many other research areas currently, there is an increasing push towards the use of the Semantic Web for publishing metadata using the Resource Description Framework (RDF). Semantic Web technologies, though, also facilitate the querying of data via the SPARQL query language, as well as logical inferencing via the careful creation and use of web ontology language (OWL) ontologies. This, in turn, opens up the intriguing possibility of deferring our decision regarding which particular type of MIR query to ask of our low-level music metadata until some point later down the line, long after all the heavy signal processing has been carried out. In this thesis, we describe an over-arching vision for an alternative MIR paradigm, built around the principles of early, studio-based metadata capture, and exploitation of open, machine-readable Semantic Web data. Using the specific example of structural segmentation, we demonstrate that by analysing multitrack rather than mixed audio, we are able to achieve a significant and quantifiable increase in the accuracy of our segmentation algorithm. We also provide details of a new multitrack audio dataset with structural segmentation annotations, created as part of this research, and available for public use. Furthermore, we show that it is possible to fully implement a pair of pattern discovery algorithms (the SIA and SIATEC algorithms { highly applicable, but not restricted to, symbolic music data analysis) using only SemanticWeb technologies { the SPARQL query language, acting on RDF data, in tandem with a small OWL ontology. We describe the challenges encountered by taking this approach, the particular solution we've arrived at, and we evaluate the implementation both in terms of its execution time, and also within the wider context of our vision for a new MIR paradigm.en_US
dc.description.sponsorshipEPSRC studentship no. EP/505054/1.en_US
dc.publisherQueen Mary University of Londonen_US
dc.subjectElectronic Engineeringen_US
dc.subjectMusic Information Retrievalen_US
dc.titleMusic Metadata Capture in the Studio from Audio and Symbolic Dataen_US
dc.rights.holderThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author

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    Theses Awarded by Queen Mary University of London

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