Sparse and shift-invariant representations of music
Blumensath, Thomas and Davies, Mike (2006) Sparse and shift-invariant representations of music. IEEE Transactions on Audio, Speech and Language Processing, 14, (1), 50-57. (doi:10.1109/TSA.2005.860346 ).
Redundancy reduction has been proposed as the main computational process in the primary sensory pathways in the mammalian brain. This idea has led to the development of sparse coding techniques, which are exploited in this article to extract salient structure from musical signals. In particular, we use a sparse coding formulation within a generative model that explicitly enforces shift-invariance. Previous work has applied these methods to relatively small problem sizes. In this paper, we present a subset selection step to reduce the computational complexity of these methods, which then enables us to use the sparse coding approach for many real world applications. We demonstrate the algorithm's potential on two tasks in music analysis: the extraction of individual notes from polyphonic piano music and single-channel blind source separation.
|Keywords:||blind source separation, independent component analysis (ica), shift-invariance, sparse coding, time–series analysis, unsupervised learning|
|Divisions:||University Structure - Pre August 2011 > School of Mathematics > Applied Mathematics
Faculty of Engineering and the Environment > Institute of Sound and Vibration Research > Signal Processing & Control Research Group
|Date Deposited:||31 Mar 2010 15:31|
|Last Modified:||11 Sep 2012 14:20|
|Contributors:||Blumensath, Thomas (Author)
Davies, Mike (Author)
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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