Javier Tapia

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


This research presents new techniques for active noise-control systems. This thesis introduces and applies these new techniques to real-time implementations with success. The active noise control is achieved based on the principle of superposition. For instance, an “anti-sound” of equal amplitude and opposite phase is generated and acoustically combined with the unwanted sound. The result is the cancellation of both sounds. One of the most difficult problems with this approach is that the “antisound" is irradiated all over the environment following an acoustical feedback to the input sensor. The approach includes this acoustic feedback as a part of the whole model. Therefore, poles introduced into the response of the whole model may be compensated for on an adaptive basis by using a pole-zero or adaptive MR filter. The presence of feedback makes filter stability an issue and can impact adversely on the algorithm’s convergence time and the general numerical sensitivity of the filter. Thus the largest obstacle to the wide use of adaptive HR filters is the lack of robustness and a well-understood algorithm for adjusting the required filter gains. This research presents a different algorithm for active noise control as well as introduces the Simplified Hyperstable Adaptive Recursive Filter (SHARF) algorithm to overcome stability problems in the adaptive HR filter. However, the acoustical environment offers more serious difficulties for the adaptive HR filter. A highly reverberant environment produces standing waves and electromechanical resonances in the feedback circuit that could cause instabilities during the adaptive process, especially at low frequencies. To overcome this problem, an optimized Automatic Gain Controller (AGC) was developed. This has a reliable behavior which makes it suitable for the application of the recursive Least Mean Square (LMS) algorithm. The algorithm used for active noise control utilizes a model of the acoustical error-path in order to converge. If this model changes for any reason, such as different extreme weather conditions and aged transducers, an on-line modeling becomes necessary in order to make the system track and compensate for those changes. For this reason a new on-line modeling technique is also developed and presented as an alternative to the existent techniques.


Includes bibliographical references (pages [99]-102)


viii, 102 pages




Northern Illinois University

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