Stephan P. Lovstedt

Bio: Stephan P. Lovstedt is an academic researcher from Brigham Young University. The author has contributed to research in topics: Active noise control & Least mean squares filter. The author has an hindex of 3, co-authored 4 publications receiving 44 citations.

Eigenvalue equalization filtered-x algorithm for the multichannel active noise control of stationary and nonstationary signals.

Abstract: The FXLMS algorithm, which is extensively used in active noise control, exhibits frequency dependent convergence behavior. This leads to degraded performance for time-varying and multiple frequency signals. A new algorithm called the eigenvalue equalization filtered-x least mean squares (EE-FXLMS) has been developed to overcome this limitation without increasing the computational burden of the controller. The algorithm is easily implemented for either single or multichannel control. The magnitude coefficients of the secondary path transfer function estimate are altered while preserving the phase. For a reference signal that has the same magnitude at all frequencies, the secondary path estimate is given a flat response over frequency. For a reference signal that contains tonal components of unequal magnitudes, the magnitude coefficients of the secondary path are adjusted to be the inverse magnitude of the reference tones. Both modifications reduce the variation in the eigenvalues of the filtered-x autocorr...

Eigenvalue equalization filtered-x ( EE-FXLMS) algorithm applied to the active minimization of tractor noise in a mock cabin

Abstract: The active control of tractor noise requires the ability to track and control a signal that changes in frequency as the speed of the engine, in revolutions per minute (rpm), changes during operation. The most common control approach is typically based on some version of the filtered-x algorithm. For this algorithm, the convergence and tracking speed are functions of the frequency dependent eigenvalues of the filtered-x autocorrelation matrix. To maintain stability, the system must be implemented based on the slowest converging frequency that will be encountered. This often leads to significant degradation in the overall performance of the control system. This paper will present an approach which largely overcomes this frequency dependent performance, maintains a relatively simple control implementation, and improves the overall performance of the control system. The control approach is called the eigenvalue equalization filtered-x least mean squares (EE-FXLMS) algorithm and its effectiveness will be demonstrated through an application to tractor noise in a mock cab. Experimental results will be presented which show that the EE-FXLMS algorithm has faster convergence times and provides on average a 1 dB increase in attenuation. A 3.5 dB increase in attenuation was seen in some of the cases presented.

Genetic Algorithm Applied to the Eigenvalue Equalization Filtered-x LMS Algorithm (EE-FXLMS)

Abstract: The FXLMS algorithm, used extensively in active noise control (ANC), exhibits frequency-dependent convergence behavior. This leads to degraded performance for time-varying tonal noise and noise with multiple stationary tones. Previous work by the authors proposed the eigenvalue equalization filtered-x least mean squares (EE-FXLMS) algorithm. For that algorithm, magnitude coefficients of the secondary path transfer function are modified to decrease variation in the eigenvalues of the filtered-x autocorrelation matrix, while preserving the phase, giving faster convergence and increasing overall attenuation. This paper revisits the EE-FXLMS algorithm, using a genetic algorithm to find magnitude coefficients that give the least variation in eigenvalues. This method overcomes some of the problems with implementing the EE-FXLMS algorithm arising from finite resolution of sampled systems. Experimental control results using the original secondary path model, and a modified secondary path model for both the previous implementation of EE-FXLMS and the genetic algorithm implementation are compared.

Eigenvalue equalization applied to the active minimization of energy density in a mock helicopter cabin

Abstract: A number of applications in active noise control require the ability to control and track multiple frequencies. If a standard filtered‐x algorithm is used, the system must be designed to be stable for the slowest converging frequency anticipated, thereby leading to reduced overall performance of the system. Previous work has focused on overcoming this through development of a method that equalizes the eigenvalues of the system over the operating frequency range, leading to more uniform performance. The current work has built on the previous work to extend the method for implementation in systems that control the acoustic energy density. Minimizing energy density has been shown to have favorable performance characteristics when used for controlling enclosed acoustic fields. Thus, combining the approach of equalizing the system eigenvalues with energy density control leads to a system that incorporates the advantages of both methods. The control approach is demonstrated through implementation in a mock helicopter cabin, to demonstrate the favorable convergence characteristics, along with the global control of the field.

Recent Advances on Active Noise Control: Open Issues and Innovative Applications

Abstract: The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

Adaptive noise control system

Abstract: An active noise cancellation system that reduces, at a listening position, power of a noise signal radiated from a noise source to the listening position. The system includes an adaptive filter, at least one acoustic actuator and a signal processing device. The adaptive filter receives a reference signal representing the noise signal, and provides a compensation signal. The at least one acoustic actuator radiates the compensation signal to the listening position. The signal processing device evaluates and assesses the stability of the adaptive filter.

Multi-Channel Active Noise Control System with Channel Equalization

Abstract: A multiple error filtered-x least mean square (MEFxLMS) algorithm using a channel equalization virtual secondary path for an active noise control/cancellation (ANC) system for treating noise in a Multiple-Input Multiple-Output (MIMO) system The channel equalization technique equalizes amplitude levels of the estimated response of all primary channels to overcome limitations caused by the frequency dependent property of standard filtered-x least mean square (FxLMS) algorithm, reduce the variation of convergence speed existed in the multiple channels, and improve the overall performance of the control system The convergence property of the algorithm is analyzed in the frequency domain