Active sound attenuation systems may be described using a system identification framework in which an adaptive filter is used to model the performance of an unknown acoustical plant. An error signal may be obtained from a location following an acoustical summing junction where the undesired noise is combined with the output of a secondary sound source. For the model output to properly converge to a value that will minimize the error signal, it is frequently necessary to determine the transfer function of the secondary sound source and the path to the error signal measurement. Since these transfer functions are unknown and continuously changing in a real system, it is desirable to perform continuous on-line modeling of the output transducer and error path. In this article, the use of an auxiliary random noise generator for this modeling is described. Based on a Galois sequence, this technique is easy to implement, provides continuous on-line modeling, and has minimal effect on the final value of the error signal.

The filtered-X algorithm developed by Widrow and Burgess is an alternate form of the least-mean-square (LMS) algorithm for use when there are transfer functions in the auxiliary path following the adaptive filter. To ensure convergence of the algorithm, the input to the error correlators is filtered by a copy of these auxiliary path transfer functions. More recently, the author has presented a new approach to active noise control in the presence of acoustic feedback that uses an infinite impulse response (IIR) filter structure with an alternate form of the recursive least-mean-square (RLMS) algorithm. This algorithm may be described as a filtered-U algorithm since it uses a copy of the auxiliary path transfer functions to filter the generalized input vector U to the error correlators of both the direct and recursive elements of the filter to ensure convergence. The relationship of the filtered-U to the filtered-X algorithm and other earlier concepts is discussed.

Active sound attenuation systems may be described using a system identification framework in which an adaptive filter is used to model the performance of an unknown acoustical plant. An error signal may be obtained from a location following an acoustical summing junction where the undesired noise is combined with the output of a secondary sound source. For the model output to properly converge to a value that will minimize the error signal, it is frequently necessary to determine the transfer function of the secondary sound source and the path to the error signal measurement. Since these transfer functions are unknown and continuously changing in a real system, it is desirable to perform continuous on-line modeling of the output transducer and error path. In this article, the use of an auxiliary random noise generator for this modeling is described. Based on a Galois sequence, this technique is easy to implement, provides continuous on-line modeling, and has minimal effect on the final value of the error signal.

A transfer-matrix approach for estimating the characteristic impedance and wave numbers of limp and rigid porous materials

No abstract