Conjugate gradient‐based active noise cancellation

Author:

de Souza Jose Victor Goncalez1ORCID,de Alcantara Andrade Fabio Augusto2ORCID,Pinto Milena Faria1,Kar Asutosh3,de Barros Ana Lucia Ferreira1,Haddad Diego Barreto1

Affiliation:

1. PPGIO Centro Federal de Educação Tecnológica Celso Suckow da Fonseca Rio de Janeiro Brazil

2. Faculty of Technology, Natural Sciences and Maritime Science University of South‐Eastern Norway Horten Norway

3. Indian Institute of Information Technology, Design and Manufacturing Kancheepuram Chennai India

Abstract

AbstractActive noise cancellation schemes should synthesize proper secondary perturbations without relevant prior information. Their performance depends strongly on their adaptive mechanisms, which, sometimes, do not fulfill the severe requirements of practical setups. This research demonstrates for the first time that the adaptive conjugate gradient optimization method can be used for active noise cancellation tasks. Additionally, a stochastic model is devised for estimating its steady‐state solution, even when the secondary path is not correctly estimated. Such a theoretical analysis demonstrates the asymptotic unbiasedness of the proposed scheme when the secondary plant is perfectly estimated. Simulations reveal that the devised method can attain better asymptotic performance and a higher convergence rate than the traditional FX‐LMS algorithm, at the expense of an increased computational burden. Furthermore, simulations show that the method outperforms the standard ones for nonstationary inputs (such as speech signals).

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Institution of Engineering and Technology (IET)

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