CUED Publications database

Efficient adaptive filtering in compressive domains for sparse systems and relation to transform-domain adaptive filtering

Buchner, H and Helwani, K and Ahmad, BI and Godsill, S (2017) Efficient adaptive filtering in compressive domains for sparse systems and relation to transform-domain adaptive filtering. In: UNSPECIFIED pp. 3859-3863..

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Abstract

© 2017 IEEE. In this paper we introduce a novel class of efficient multichannel adaptive filtering algorithms for sparse FIR systems. By suitably integrating ideas from compressed sensing and adaptive filter theory, this class of algorithms allows to significantly reduce the actual number of adaptive coefficients in an efficient way. These algorithms, termed compressive-domain adaptive filters, can be interpreted as a novel type of transform-domain techniques. They can also be seen as adaptive approach in an efficiently self-learning manifold based on the prior knowledge of sparseness of the system. An important property of this concept is that it does not place additional restrictions on the input signal characteristics. Based on the well-known RLS algorithm as a reference, the simulation results confirm that the proposed algorithm converges at acceptable rates, even for strongly colored signals such as speech and audio.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div C > Engineering Design
Depositing User: Cron Job
Date Deposited: 01 Aug 2017 02:17
Last Modified: 03 Aug 2017 03:00
DOI: