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Sparse and shift-invariant representations of music

Sparse and shift-invariant representations of music
Sparse and shift-invariant representations of music
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.

blind source separation, independent component analysis (ica), shift-invariance, sparse coding, time–series analysis, unsupervised learning
1558-7916
50-57
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Davies, Mike
3f1c4097-ef54-4f66-a4b9-dbca705775a4
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Davies, Mike
3f1c4097-ef54-4f66-a4b9-dbca705775a4

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).

Record type: Article

Abstract

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.

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Published date: January 2006
Keywords: blind source separation, independent component analysis (ica), shift-invariance, sparse coding, time–series analysis, unsupervised learning
Organisations: Signal Processing & Control Grp

Identifiers

Local EPrints ID: 142533
URI: https://eprints.soton.ac.uk/id/eprint/142533
ISSN: 1558-7916
PURE UUID: dc3e9bc9-beb3-4a59-b166-61941c0465a7

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Date deposited: 31 Mar 2010 15:31
Last modified: 18 Jul 2017 23:11

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