In an active noise control system that uses the Filtered‐x method as an adaptive algorithm, the impulse response on the error path is observed and the result is assigned as a coefficient of an error path filter before the system is started. Such an impulse response can obviously change after the system is started. The change can increase the difference between the assigned coefficient and the inherent coefficient of the error path filter, and thus render the operation of noise control unstable. This paper proposes a noise control filter coefficient renewal method that does not require the calculation of an error path filter coefficient by focusing on the fact that, when a set of two different coefficients is assigned to a noise control filter, the system consisting of components from a noise detection microphone to an error detection microphone establishes two independent equations whose variables are impulse responses for the feedforward path and the error path system. The coefficient of the noise control filer can be renewed by solving the simultaneous equations by iteration. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 85(12): 101–108, 2002; Published online in Wiley InterScience (www.interscience.wiley. com). DOI 10.1002/ecjc.1132
A model for texture description, called "Primitive and Point Configuration (PPC) texture model," and an estimation method of the primitive, which is an elementary object for configuring a texture, are proposed in this paper. The PPC texture model regards that a texture is composed by arranging grains that are derived from one or a few primitives by some modification. The primitive shape is estimated by the principle that the primitive resembling the grains best should be the optimal estimation. This estimation is achieved by finding the structuring element that minimizes the integral of the size distribution function of a target texture.
In this study, we verify the performance of the simultaneous equations method using an experimental active noise control system. The simultaneous equations method is based on a priciple different from the filtered-x algorithm requiring a filter modeled on a secondary path from a loudspeaker to an error microphone. Instead of the filter, called the secondary path filter, this method uses an auxiliary filter identifying the overall path consisting of a primary path, a noise control filter and the secondary path. As inferred from the configuration of the overall path, the auxiliary filter can provide two independent equations when two different coefficient vectors are given to the noise control filter. The method thereby estimates the coefficient vector of the noise control filter minimizing the output of the error microphone. In this paper, we propose the application of a frequency domain adaptive algorithm to the identification of the overall path. An improvement in the noise reduction speed is thereby expected. In this paper, we also present computer simulation results demonstrating that the simultaneous equations method can automatically recover the noise reduction effect degraded by path changes, and finally, using an experimental system, we indicate that the method successfully works in practical systems.
SUMMARYThe reference signals used in a multichannel system are usually strongly crosscorrelated. Preprocessing is normally added to reduce the crosscorrelation between the reference signals because this correlation makes estimating the adaptive filter coefficients difficult. However, this preprocessing is equivalent to warping the reference signals. The transmission of these warped reference signals to an unknown system hinders the essential system operation. On the other hand, it is necessary for uncorrelated components between the reference signals to exist when estimating the adaptive filter coefficients. Preprocessing that increases the ratio of these components is important in improving the convergence characteristics. In this paper, we propose an algorithm that does not transmit preprocessed reference signals to an unknown system, but only uses them to estimate the adaptive filter coefficients, and explain the design conditions needed to implement this algorithm. In other words, with the condition of not increasing the estimation errors, we derive the optimum values of the coefficients that reduce the correlation between the reference signals, which is a feature of this algorithm, and verify its effectiveness in simulations. By employing the proposed algorithm, improvements in the convergence characteristics can be designed without affecting the essential operation of the system.
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