There is a great deal of effort (and literature) on preparing undergraduate students to work on research-based problems in the area of signal processing during their undergraduate studies. However, most of the undergraduate students are in fact not ready to carry out some meaningful research until their graduation. One possible reason could be that most of the research problems in the area of signal processing require significant background knowledge in advanced linear algebra and stochastic processes. As a result, students are typically assigned mini-projects (derived from a larger problem being solved), or student-driven design projects in some independent study course. It is expected that after graduating, they will be ready and motivated to do research during their graduate-level higher studies. The focus of this paper, on the other hand, is to present the efforts and results on engaging students in doing research during their undergraduate studies. The Electrical and Computer Engineering (ECE) Department at Nazarbayev University (NU) attracts some of the best students in the country. Considering their potential and motivation level, a strategy has envisaged to engage them for doing research during the early stage of their undergraduate studies. The results of this strategy have been very promising. The selected undergraduate students learned to do independent research in the area of signal processing (and machine learning). Furthermore, their research work has resulted in numerous publications in top-ranked journals and conferences.

This paper proposes a new approach for the signal denoising method based on empirical mode decomposition (EMD) and a novel metric based on detrended fluctuation analysis (DFA). The proposed denoising method first obtains data-driven signal representation called intrinsic mode functions (IMFs) using EMD. Next, DFA is used to threshold the predominantly noisy modes based on their scaling exponent and higher frequency content. Finally, using a shrinkage method on predominantly noisy modes, the denoised signal is reconstructed by the combination of the denoised modes and the remaining IMFs which were obtained as an output from the first step. A comprehensive simulation-based study demonstrates the superiority of the proposed method in comparison to the state-of-the-art methods in signal denoising. Furthermore, the proposed denoising scheme exhibits promising denoising results in practical applications for electrocardiogram (ECG) signals.

This paper investigates a new recursive least squares (RLS) algorithm for active control (AC) of impulsive noise sources. The proposed RLS algorithm is based on an objective function having robustness against the impulsive noise. This paper introduces two versions of the proposed algorithm. In the first version, the update equation comprises a fixed step-size to regulate the adaptation. Considering that a fix-valued step-size results in a trade-off situation from the view-point of the performance during the transient and at the steady-state, a second version of proposed algorithm addresses this trade-off by incorporating a convex combined step-size (CCSS) strategy. The CCSS strategy selects (automatically) a large-valued step-size at the start-up for a fast convergence speed. As the AC system approaches the steady-state, the CCSS is adapted to a small value for improving the performance at the steady-state. The simulation results show that the proposed algorithm is very effective in the various settings of AC systems for impulsive sources.

In this paper, we investigate active noise control systems, which use auxiliary white Gaussian noise for online feedback path modeling and neutralization. Such systems comprise three adaptive filters: 1) active noise control filter, 2) linear prediction filter, and 3) feedback path modeling and neutralization filter. The injection of white Gaussian noise with large/small variance improves/degrades the convergence of feedback path modeling and neutralization filter but on the other hand degrades/improves the noise-reduction performance of active noise control systems. The focus of this paper is to have an adequate compromise between the convergence of feedback path modeling and neutralization filter and the noise-reduction-performance of active noise control systems. In this paper: 1) A tuning free gain scheduling of white Gaussian noise is used to improve the noise-reduction performance of active noise control systems, and 2) a variable step-size is used to compensate for the decrease in the convergence of feedback path modeling and neutralization filter due to gain scheduling. The proposed idea is explained for a single channel as well as for multichannel active noise control systems. Computer simulations have been carried out to demonstrate the performance of the proposed method for a single channel as well as for multichannel active noise control systems.

In this paper, we propose a novel solution for continuous acoustic feedback neutralization in the digital hearing aids. The proposed method is based on two adaptive filters working in tandem. The error signal of the first adaptive filter is used as a desired response for the second adaptive filter being excited by a low-level (constant) probe signal. Initially at the start up, the first adaptive filter gives a fast convergence, however, its input and desired response are correlated with each other and it may converge to a biased solution at the steady-state. On the other hand, the second adaptive filter, though converging slowly being excited by a low-level probe signal, would give a good steady-state estimate of the acoustic feedback path. A novel strategy is proposed to exchange the weights between the two adaptive filters, such that the misconvergence of the first adaptive filter is avoided, as well as the initial convergence of the second adaptive filter is improved. Computer simulations demonstrate the improved performance of the proposed method.

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.

This paper deals with the auxiliary noise-based methods for active noise control (ANC) systems with online secondary path modeling (SPM). The proposed method comprises two adaptive filters: the modified Filtered-X normalized least-mean-square algorithm-based ANC filter, and the normalized least-mean-square algorithm-based SPM filter excited by auxiliary noise. The auxiliary noise injected for online SPM, degrades the noise-reduction performance of the ANC system. A two-stage gain scheduling strategy is proposed to vary power of the auxiliary noise. In the first stage the gain is varied on the basis of power of the error signal of SPM filter, and in the second stage the gain is varied on the basis of the correlation estimate of the two adjacent samples of the error signal of SPM filter. The main idea is to inject large-power auxiliary noise at the start up or when a change in the acoustic paths is detected, and to reduce the power as the system converges. The proposed method achieves a fast convergence of the SPM filter and gives a robust performance in the presence of strong perturbation in acoustic paths. Furthermore, the proposed method improves the noise-reduction performance at steady-state even in the presence of an uncorrelated disturbance at the error microphone. Moreover, the improved performance is achieved at a lower computational cost as compared with a recent method proposed in [A. Carini, and S. Malatini, “Optimal variable step-size NLMS algorithms with auxiliary noise power scheduling for feedforward active noise control,”IEEE Trans. Audio, Speech Lang. Process., vol. 16, no. 8, pp. 1383-1395, Nov. 2008]. Extensive simulations are carried out to verify the effectiveness of the proposed method.

Auxiliary noise, injected in active noise control (ANC) system for on-line secondary path modeling (SPM), contributes to the residual error (which we want to minimize). In this paper, two new schemes for controlling the auxiliary noise injection are proposed. The first method is ON/OFF control in which an additional fixed filter is used to temporarily hold the tap-weights of the adaptive on-line SPM filter. The decision (ON/OFF) is carried out on the basis of difference of power of errors from the two filters. After suspension of the auxiliary noise injection, the algorithm continuously checks for error outliers so that injection may be restarted in the case of path perturbation. In the second method a new auxiliary-noise-power scheduling strategy based on the error signal of the SPM filter is proposed. In both proposed schemes a fixed step-size is used for the SPM filter and the noise control filter, which reduces the computational complexity of the algorithms. In addition to this, in the second method, no off-line modeling (initial phase) of the secondary path is required.

This study proposes and evaluates a recursive algorithm for incremental estimation of independent components from on-line data. The algorithm offers the convergence properties of batch independent component analysis (ICA) with incremental updates of a form similar to natural gradient (NG) on-line information maximization (Infomax). We employ recursive procedure to arrive at steady state solution given by NG Infomax. Furthermore, we propose a novel procedure to compute corrective updates on the basis of previous estimates. Implementation of this algorithm incurs linear complexity in data size, input dimensions, and number of estimated independent components. Significant gains in convergence rate over on-line natural gradient ICA are demonstrated.

In filtered-x LMS (FxLMS) single-channel feedforward active noise control (ANC) systems, a reference signal is available that is correlated with the primary disturbance at the error microphone. In some practical situations, there may also be a disturbance uncorrelated with the primary disturbance at the error microphone, for which a correlated reference signal is not available. This disturbance, being uncorrelated with the primary noise, cannot be controlled by the standard FxLMS algorithm, and increases the residual noise. In this paper we propose an improved hybrid ANC system that can simultaneously control both the correlated and uncorrelated noise signals. The proposed method comprises three adaptive filters: 1) the FxLMS-based ANC filter to cancel the primary noise; 2) a separate FxLMS-based ANC filter to cancel the uncorrelated disturbance; and 3) an LMS-based supporting adaptive filter to generate appropriate signals for the two ANC filters. Computer simulations demonstrate that the proposed method can effectively mitigate the correlated and uncorrelated primary disturbances. This improved performance is achieved at only a small increase in computational complexity.

The paper concerns active control of impulsive noise. The most famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems is based on the minimization of variance of error signal. The impulsive noise can be modeled using non-Gaussian stable process for which second order moments do not exist. The FxLMS algorithm, therefore, becomes unstable for the impulsive noise. Among the existing algorithms for ANC of impulsive noise, one is based on the minimizing least mean p-power (LMP) of the error signal, resulting in FxLMP algorithm. The other is based on modifying; on the basis of statistical properties; the reference signal in the update equation of the FxLMS algorithm. In this paper, the proposed algorithm is a modification and combination of these two approaches. Extensive simulations are carried out, which demonstrate the effectiveness of the proposed algorithm. It achieves the best performance among the existing algorithms, and at the same computational complexity as that of FxLMP algorithm.

The disturbance picked up by error microphone can significantly degrade the steady-state performance of active noise control (ANC) systems. Previously a cascading algorithm has been proposed, where a supporting adaptive filter is used for removing the uncorrelated disturbance from the error signal of filtered-x least mean square (FxLMS) algorithm. We modify this algorithm to improve the performance in terms of fast convergence speed and reduced steady-state fluctuations. We modify the desired response of the supporting filter which gives us better control to tune its parameters. This modification allows to use modified FxLMS (MFxLMS) algorithm to adapt ANC filter, which results in improved convergence speed. Computer simulations are carried out which demonstrate the effectiveness of the proposed method. This improved performance is achieved at an increased computational complexity.

The presence of strong acoustic feedback degrades the convergence speed of the active noise control (ANC) filter, and in the worst case the ANC system may become unstable. A fixed feedback neutralization filter, obtained offline, can be used to neutralize the acoustic feedback. The feedback path, however, may be time varying, and we may need continual adjustments during online operation of the ANC system. This paper proposes a new method for online modeling of the acoustic feedback path in ANC systems. The proposed method uses three adaptive filters; a noise control filter, a feedback path modeling (FBPM) filter, and an adaptive noise cancelation (ADNC) filter. The objective of ADNC filter is to remove the disturbance from the desired response of FBPM filter. In comparison with the existing method, which works only for predictable noise sources, the proposed method can work, as well, with the broadband noise sources. The computer simulations are carried out for narrowband (predictable) (case I) and broadband (random) noise sources (case II). It is demonstrated that the proposed method performs better than the existing method in both cases

This paper proposes a new method for online secondary path modeling in active noise control systems. The existing methods for active noise control systems with online secondary path modeling consist of three adaptive filters. The main feature of the proposed method is that it uses only two adaptive filters. In the proposed method, the modified-FxLMS (MFxLMS) algorithm is used in adapting the noise control filter and a new variable step size (VSS) least mean square (LMS) algorithm is proposed for adaptation of the secondary path modeling filter. This VSS LMS algorithm is different from the normalized-LMS (NLMS) algorithm, where the step size is varied in accordance with the power of the reference signal. Here, on the other hand, the step size is varied in accordance with the power of the disturbance signal in the desired response of the modeling filter. The basic idea of the proposed VSS algorithm stems from the fact that the disturbance signal in the desired response of the modeling filter is decreasing in nature, (ideally) converging to zero. Hence, a small step size is used initially and later its value is increased accordingly. The disturbance signal, however, is not available directly, and we propose an indirect method to track its variations. Computer simulations show that the proposed method gives better performance than the existing methods. This improved performance is achieved at the cost of a slightly increased computational complexity.

This paper proposes a new structure for feedforward active noise control (ANC) systems with online secondary path modeling. The proposed method: 1) uses the same error signal for updating the noise control process as used for the secondary path modeling process and 2) incorporates an adaptive filtering with averaging based filtered-reference algorithm in the noise control process. The computer simulations have been conducted with both narrowband and broadband noise signals. It is shown that in the proposed ANC system the residual noise signal and the secondary-path-modeling error can be reduced at a faster convergence rate than the existing methods. This improved performance is achieved at the expense of a slightly increased computational complexity.

This letter proposes an adaptive filtering with averaging-based algorithm for active noise control (ANC) systems. This algorithm uses a similar structure as that of the FxLMS-based ANC system. The proposed algorithm, called Filtered-x Adaptive Filtering with Averaging (FxAFA) algorithm, uses averages of both data and correction terms to find the updated values of the tap weights of the ANC controller. The computer simulations are conducted for single-channel feedforward ANC systems. It is shown that the proposed algorithm gives fast convergence as compared with the FxLMS algorithm and achieves better performance in the presence of the measurement noise. The comparison with the FxRLS algorithm shows that the proposed FxAFA algorithm is a better choice for low computational complexity and stable performance.