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Unformatted text preview: 34 IEEE SIGNAL PROCESSING LETTERS, VOL. 16, NO. 1, JANUARY 2009 A Novel LMS Algorithm Applied to Adaptive Noise Cancellation J. M. Górriz, Javier Ramírez, S. Cruces-Alvarez, Carlos G. Puntonet, Elmar W. Lang, and Deniz Erdogmus , Senior Member, IEEE Abstract— In this letter, we propose a novel least-mean-square (LMS) algorithm for filtering speech sounds in the adaptive noise cancellation (ANC) problem. It is based on the minimization of the squared Euclidean norm of the difference weight vector under a stability constraint defined over the a posteriori estimation error. To this purpose, the Lagrangian methodology has been used in order to propose a nonlinear adaptation rule defined in terms of the product of differential inputs and errors which means a gen- eralization of the normalized (N)LMS algorithm. The proposed method yields better tracking ability in this context as shown in the experiments which are carried out on the AURORA 2 and 3 speech databases. They provide an extensive performance evalu- ation along with an exhaustive comparison to standard LMS al- gorithms with almost the same computational load, including the NLMS and other recently reported LMS algorithms such as the modified (M)-NLMS, the error nonlinearity (EN)-LMS, or the nor- malized data nonlinearity (NDN)-LMS adaptation. Index Terms— Adaptive noise canceler., least-mean-square (LMS) algorithm, speech enhancement, stability constraint. I. INTRODUCTION T HE widely used least-mean-square (LMS) algorithm has been successfully applied to many filtering applications, including signal modeling, equalization, control, echo can- cellation, biomedicine, or beamforming –. The typical noise cancellation scheme is shown in Fig. 1. Two distant microphones are needed for such application to capture the nature of the noise and the speech sound simultaneously. The correlation between the additive noise that corrupts the clean speech (primary signal) and the random noise in the reference input (adaptive filter input) is necessary to adaptively cancel the noise of the primary signal. The adjustable weights are typically determined by the LMS algorithm  because of its simplicity, ease of implementation, and low computational complexity. The weight update equation for the adaptive noise canceler (ANC) is (1) Manuscript received August 04, 2008; revised October 05, 2008. Current version published December 12, 2008. This work was supported in part by the PETRI DENCLASES (PET2006-0253), TEC2007-68030-C02-01, and TEC2008-02113 projects of the Spanish MEC and in part by the Excellence Project (TIC-02566) of the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain). This work was written in English with the help of Mrs....
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This note was uploaded on 01/27/2010 for the course EE 4343 taught by Professor Asdfasdsas during the Spring '10 term at Aarhus Universitet.
- Spring '10
- Signal Processing