Kavita Char

Publication Date


Document Type


First Advisor

Kuo, Sen M. (Sen-Maw)

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Electrical Engineering


Noise control; Acoustical engineering; Convergence


This thesis investigates various fast convergence adaptive algorithms for an active noise control (ANC) application. The traditional filtered-X LMS algorithm has been implemented first, and its performance compared with various adaptive algorithms. Results are compared on the basis of rate of convergence, steady-state error and complexity of the algorithm. Two new algorithms, based on the transversal filter structure, the correlation LMS and the gradient step-size algorithms, have been implemented and analyzed. A new configuration for ANC has been suggested using the lattice filter structure, in order to increase the convergence rate in case of signals having large eigenvalue spreads. An alternative configuration using the lattice filter that yields the same performance as the first configuration, yet gives considerable saving in computation, has also been proposed. All the adaptive algorithms have been verified using computer simulations. The results obtained have been validated by real-time experiments using a floating point DSP, the TMS320C30. The results included in this thesis show that the algorithms are practical and well suited for use in an ANC application.


Includes bibliographical references (pages [131]-132)


xi, 132 pages




Northern Illinois University

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