Adaptive noise cancellation using fast optimum block algorithms

Author

Deisher, Michael E. ; Spanias, Andreas S.

Author_Institution

Dept. of Electr. Eng., Arizona State Univ., Tempe, AZ, USA

fYear

1991

fDate

11-14 Jun 1991

Firstpage

698

Abstract

The application of block frequency domain (FFT-based) adaptive algorithms to noise cancellation is considered. Two classes of FFT-based algorithms are examined, namely those associated with periodic convolution and those associated with linear convolution. In both cases normalized optimum convergence factors are considered. Experimental noise cancellation results are given using speech corrupted by white, colored, and harmonically structured noise (simulated engine noise). Quantitative and subjective evaluations are given for all the results, and trade-offs of performance versus computational complexity are established

Keywords

adaptive filters; convergence of numerical methods; fast Fourier transforms; interference suppression; signal processing; speech analysis and processing; white noise; FFT-based algorithms; adaptive algorithms; coloured noise; computational complexity; harmonically structured noise; linear convolution; noise cancellation; optimum block algorithms; optimum convergence factors; periodic convolution; simulated engine noise; speech; white noise; Adaptive algorithm; Colored noise; Computational complexity; Computational modeling; Convergence; Convolution; Engines; Frequency domain analysis; Noise cancellation; Speech enhancement;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1991., IEEE International Sympoisum on

Print_ISBN

0-7803-0050-5

Type

conf

DOI

10.1109/ISCAS.1991.176430

Filename

176430