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
50-57
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Davies, Mike
3f1c4097-ef54-4f66-a4b9-dbca705775a4
January 2006
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), .
(doi:10.1109/TSA.2005.860346).
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.
Text
BDTASL06.pdf
- Other
More information
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: http://eprints.soton.ac.uk/id/eprint/142533
ISSN: 1558-7916
PURE UUID: dc3e9bc9-beb3-4a59-b166-61941c0465a7
Catalogue record
Date deposited: 31 Mar 2010 15:31
Last modified: 14 Mar 2024 02:55
Export record
Altmetrics
Contributors
Author:
Mike Davies
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
