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Quantized sparse approximation with iterative thresholding for audio coding

Quantized sparse approximation with iterative thresholding for audio coding
Quantized sparse approximation with iterative thresholding for audio coding
Sparse coding is a new field in signal processing with possible applications to source coding. In this paper we present a new method that combines the problems of sparse signal approximation with coefficient quantization. This method uses overcomplete dictionaries and exploits signal redundancy. The proposed method will be derived as an extension of a recently presented method (iterative thresholding) to find sparse representations of signals. Because in digital communication and storage we need a quantized representation of the signal, instead of quantization of sparse representations a posteriori, we propose a refined method that combines sparse approximation and quantization. To compare the proposed method to a posteriori quantization, we present an audio example.
Yaghoobi, M.
093ccfdd-9ba5-4d02-abb1-7f341eed070d
Blumensath, T.
470d9055-0373-457e-bf80-4389f8ec4ead
Davies, M.
ad39b2b8-121a-49ee-8e4a-daf601ba7fe6
Yaghoobi, M.
093ccfdd-9ba5-4d02-abb1-7f341eed070d
Blumensath, T.
470d9055-0373-457e-bf80-4389f8ec4ead
Davies, M.
ad39b2b8-121a-49ee-8e4a-daf601ba7fe6

Yaghoobi, M., Blumensath, T. and Davies, M. (2007) Quantized sparse approximation with iterative thresholding for audio coding. IEEE International Conference on Acoustics, Speech and Signal Processing, United States.

Record type: Conference or Workshop Item (Paper)

Abstract

Sparse coding is a new field in signal processing with possible applications to source coding. In this paper we present a new method that combines the problems of sparse signal approximation with coefficient quantization. This method uses overcomplete dictionaries and exploits signal redundancy. The proposed method will be derived as an extension of a recently presented method (iterative thresholding) to find sparse representations of signals. Because in digital communication and storage we need a quantized representation of the signal, instead of quantization of sparse representations a posteriori, we propose a refined method that combines sparse approximation and quantization. To compare the proposed method to a posteriori quantization, we present an audio example.

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More information

Published date: April 2007
Venue - Dates: IEEE International Conference on Acoustics, Speech and Signal Processing, United States, 2007-04-01
Organisations: Signal Processing & Control Grp

Identifiers

Local EPrints ID: 151923
URI: https://eprints.soton.ac.uk/id/eprint/151923
PURE UUID: df093b62-ec4d-4b77-8bdb-e9370bd977ba

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Date deposited: 15 Jun 2010 08:36
Last modified: 17 Jul 2019 00:00

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