The reference paths from original sources to reference sensors in multichannel feedforward active noise control (ANC) systems are often ignored by most ANC algorithms. Two blind preprocessing adaptive algorithms in time and frequency domain are proposed to deal with some more complicated applications, especially when the reference sensors cannot be located closely to noise sources or the noise sources are moving slowly. Blind preprocessing algorithm to the reference signals can improve the structure of the cross spectral density matrix of the inputs to the multichannel filtered-x least mean square (LMS) algorithm in the following stage, and faster convergence speed can be obtained. The computational complexity of two proposed algorithms is analysed and simulations with impulse responses measured in a real reverberant room is applied to verify the convergence performance of the proposed algorithm.

This study is focused on the necessity to improve the performance of the affine projection (AP) algorithm for active noise control (ANC) applications. The proposed algorithms are evaluated regarding their steady-state behaviour, their convergence speed and their computational complexity. To this end, different strategies recently applied to the AP for channel identification are proposed for multichannel ANC. These strategies are based either on a variable step size, an evolving projection order, or the combination of both strategies. The developed efficient versions of the AP algorithm use the modified filtered-x structure, which exhibits faster convergence than other filtering schemes. Simulation results show that the proposed approaches exhibit better performance than the conventional AP algorithm and represent a meaningful choice for practical multichannel ANC applications.

A bank of adaptive filters is updated via a parallel or direct/parallel narrowband active noise control (ANC) system uses an error signal. This work presents a delayless filter bank to separate the frequency components of the error signal based on the sinusoidal frequencies of input signals to update the corresponding adaptive filters in a direct/parallel structure. A cost function is also proposed to develop an adaptive algorithm in order to reduce noise and increase convergence speed for this complete direct/parallel narrowband ANC system. Theoretical analysis results demonstrate the increased convergence speed. Moreover, the analysis results and increased noise reduction are verified based on simulation results.

This study represents a stochastic model for the adaptation process performed on adaptive control systems by the filtered-x least-mean-square (FxLMS) algorithm. The main distinction of this model is that it is derived without using conventional simplifying assumptions regarding the physical plant to be controlled. This model is then used to derive a set of closed-form mathematical expressions for formulating steady-state performance, stability condition and learning rate of the FxLMS adaptation process. These expressions are the most general expressions, which have been proposed so far. It is shown that some previously derived expressions can be obtained from the proposed expressions as special and simplified cases. In addition to computer simulations, different experiments with a real-time control setup confirm the validity of the theoretical findings.

Digital active noise control (ANC) for headphones usually has to predict the noise because of the latency of common audio converters. In adaptive feedback ANC, the prediction is based on the noise that entered the headphone. This noise is low-pass filtered because of the physical barrier of the ear cups. In this study, this low-pass characteristic is exploited to define a prediction filter which does not require real-time updates. For broadband noises, the prediction filter performs better than adaptive prediction methods like the least mean squares algorithm or iterated one-step predictions in the relevant frequency band. This is shown in simulations as well as in measurements. In addition, the authors show that their prediction filter is more robust against changes in the acoustics of the headphone.

The main focus of this paper is the acoustic feedback path neutralisation during online operation of the single-channel active noise control (ANC) systems. Invasive techniques, in which additive auxiliary noise is injected for online feedback path modelling (FBPM), degrade the noise-reduction performance of ANC systems. In the existing methods for online FBPM, additive auxiliary noise with fixed variance is injected during all operating conditions of ANC systems. In this paper, a scheduling strategy is proposed to have time-varying gain for additive auxiliary noise. The time-varying gain is computed on the basis of (i) the convergence status of the FBPM filter, and (ii) the power of the interference term in the error signal of the FBPM filter. Simulation results show that, compared to the existing methods, the proposed method can better achieve the conflicting requirements of fast convergence of the FBPM filter, and reduced steady-state power of the residual error at the error microphone. For sake of completeness, appendix presents typical simulation results when both the secondary path and feedback path are simultaneously identified during online operation of ANC systems.

Most algorithms for active impulsive noise control employ non-linear transformations to limit the reference and/or error signals and to maintain system stability. From a more direct manner, a new cost function is proposed in this study which is defined as the summation of the squared Euclidean norm of difference between the currently updated filter coefficients vector and all past filter coefficients vector subject to the constraint imposed on the adaptive filter output. A new adaptive algorithm is derived from the cost function and called the filtered weight filtered-x normalised least mean square algorithm because it can be interpreted as a post filter structure which passes the filter coefficients through a first-order infinite impulse response filter. The proposed algorithm is suitable for active control of impulsive noise since it directly limits the dynamic range of the adaptive filter coefficients and prevents heavy fluctuation of the filter coefficients. Simulations compare the performance of the proposed algorithm with the existing algorithms and demonstrate the effectiveness of the proposed algorithm.