Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Optimal Time-Domain Noise Reduction FiltersSingle-Channel Noise Reduction with a Rectangular Filtering Matrix

Optimal Time-Domain Noise Reduction Filters: Single-Channel Noise Reduction with a Rectangular... [In the previous chapter, we tried to estimate one sample only at a time from the observation signal vector. In this part, we are going to estimate more than one sample at a time. As a result, we now deal with a rectangular filtering matrix instead of a filtering vector. If M is the number of samples to be estimated and L is the length of the observation signal vector, then the size of the filtering matrix is M × L. Also, this approach is more general and all the results from Chap. 2 are particular cases of the results derived in this chapter by just setting M = 1. The signal model is the same as in Sect. 2.1; so we start by explaining the principle of linear filtering with a rectangular matrix.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Optimal Time-Domain Noise Reduction FiltersSingle-Channel Noise Reduction with a Rectangular Filtering Matrix

Download PDF
19 pages

Loading next page...
 
/lp/springer-journals/optimal-time-domain-noise-reduction-filters-single-channel-noise-k3lCSMXbEi
Publisher
Springer Berlin Heidelberg
Copyright
© Jacob Benesty 2011
ISBN
978-3-642-19600-3
Pages
23 –41
DOI
10.1007/978-3-642-19601-0_3
Publisher site
See Chapter on Publisher Site

Abstract

[In the previous chapter, we tried to estimate one sample only at a time from the observation signal vector. In this part, we are going to estimate more than one sample at a time. As a result, we now deal with a rectangular filtering matrix instead of a filtering vector. If M is the number of samples to be estimated and L is the length of the observation signal vector, then the size of the filtering matrix is M × L. Also, this approach is more general and all the results from Chap. 2 are particular cases of the results derived in this chapter by just setting M = 1. The signal model is the same as in Sect. 2.1; so we start by explaining the principle of linear filtering with a rectangular matrix.]

Published: Apr 16, 2011

Keywords: Noise Reduction; Signal Vector; Residual Noise; Rectangular Matrix; Coherent Noise

There are no references for this article.