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dc.contributor.authorO'Hanlon, Ken
dc.date.accessioned2015-09-22T12:41:19Z
dc.date.available2015-09-22T12:41:19Z
dc.date.issued2014-05-02
dc.identifier.citationO'Hanlon., K.. 2014. Automatic Music Transcription using Structure and Sparsity. Queen Mary University of Londonen_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/8818
dc.descriptionPhden_US
dc.description.abstractAutomatic Music Transcription seeks a machine understanding of a musical signal in terms of pitch-time activations. One popular approach to this problem is the use of spectrogram decompositions, whereby a signal matrix is decomposed over a dictionary of spectral templates, each representing a note. Typically the decomposition is performed using gradient descent based methods, performed using multiplicative updates based on Non-negative Matrix Factorisation (NMF). The final representation may be expected to be sparse, as the musical signal itself is considered to consist of few active notes. In this thesis some concepts that are familiar in the sparse representations literature are introduced to the AMT problem. Structured sparsity assumes that certain atoms tend to be active together. In the context of AMT this affords the use of subspace modelling of notes, and non-negative group sparse algorithms are proposed in order to exploit the greater modelling capability introduced. Stepwise methods are often used for decomposing sparse signals and their use for AMT has previously been limited. Some new approaches to AMT are proposed by incorporation of stepwise optimal approaches with promising results seen. Dictionary coherence is used to provide recovery conditions for sparse algorithms. While such guarantees are not possible in the context of AMT, it is found that coherence is a useful parameter to consider, affording improved performance in spectrogram decompositions.en_US
dc.language.isoenen_US
dc.publisherQueen Mary University of Londonen_US
dc.subjectElectronic Engineeringen_US
dc.subjectAutomatic Music Transcriptionen_US
dc.subjectC4DM
dc.titleAutomatic Music Transcription using Structure and Sparsityen_US
dc.typeThesisen_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 [2761]
    Theses Awarded by Queen Mary University of London

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