A new hybrid active noise control (ANC, HANC) system is proposed in this paper that is equipped with an input-power-controlled online secondary-path modeling (OSPM) subsystem. An FIR linear prediction filter (LPF) is newly included that takes the FIR supporting filter (SF) error as its desired signal and separates the remaining target narrowband noise component from all the other broadband noise components. Placed right after the LPF is the OSPM subsystem. The SF and LPF output signals, namely the target broadband and narrowband components that remain in the residual error are not only used to update the feedforward and feedback ANC (FFANC, FBANC) subcontroller, respectively, but are also adopted to control the power of the OSPM-exclusive auxiliary white Gaussian noise (AWGN) to pursue a trade-off between the OSPM quality and the AWGN contribution to the residual error. The OSPM error is utilized to simultaneously update not only the OSPM subsystem but also the SF and the LPF. Due to inclusion of the LPF, the adverse coupling effects among the FFANC, the FBANC and the OSPM is reduced substantially, leaving a possibility for improving the HANC overall convergence and noise reduction performance (NRP). Furthermore, preliminary steady-state analysis of the LPF is also conducted to reveal its properties and effectiveness. Extensive simulations with both synthetical and real settings are provided and conducted to verify that the proposed HANC system is superior to existing solutions.

Abstract In this study, a robust feedforward hybrid active noise control (ANC) system with online secondary‐path modelling (SPM) is proposed that is capable of not only effectively suppressing the broadband and narrowband noise components but also tracking the secondary path (SP) variations. An finite impulse response online SPM subsystem as well as an efficient decoupling filter are included in the proposed feedforward hybrid ANC (HANC) system. The decoupling filter is a parallel‐form bandpass filter bank that consists of multiple bandpass filters that are derived from the second‐order infinite impulse response notch filters. It takes the residual noise as its input and separates the broadband component from the narrowband component, with the former used as not only a desired signal for the SPM but also as an error signal for updating the broadband sub‐controller, whereas the latter adapted simultaneously to scale the auxiliary white Gaussian noise and to update the narrowband sub‐controller. Extensive simulations are conducted with both the synthetic and real SPs as well as the synthetic and real noise signals that are generated by a large‐scale factory cutting machine (strand‐cutter) to demonstrate the advantages and effectiveness of the proposed feedforward HANC system. Comparisons are also demonstrated with the original HANC system as well as its directly extended version with an online SPM subsystem.

In this letter, a robust feedback active noise control (FBANC) system with an online secondary-path modeling (OSPM) subsystem is proposed for suppressing sinusoidal noise signals. First, a decoupling scheme is proposed by using of an adaptive linear prediction filter (LPF) that takes the FBANC residual noise as its input and separates the remaining sinusoidal noise from the broadband noise that includes an inherent additive noise within the primary noise as well as a broadband component due to an auxiliary white Gaussian noise (AWGN). The remaining sinusoidal noise (LPF output) is used effectively not only to update the FIR controller but also to scale the AWGN, while the broadband noise (LPF error) serves as a desired signal for the OSPM. In this way, the hostile coupling between the FIR controller and the OSPM subsystem is significantly mitigated, leading to both faster system convergence and improved noise reduction performance (NRP). Second, a simple and practical scheme for refreshing the proposed FBANC system is also developed to enhance its robustness against even abrupt changes with the SP and/or the primary noise signal. Extensive simulations with synthetic and real secondary paths and noise signals are conducted to demonstrate the effectiveness of the proposed FBANC system.

Second-order IIR adaptive notch filters (ANF) andbanks (ANFB) have been extensively utilized in many applications ranging from biomedical engineering to control systems. In this paper, a new efficient ANFB is proposed that is composed of two filter banks, namely a prefilter bank with cascaded IIR notch filters and a parallel structure IIR ANFB. The second-order IIR ANFs or filter bank cells in the proposed ANFB are updated by utilizing a modified normalized gradient (ModNG) algorithm. The proposed ANFB requires the same number of cells as does a conventional parallel form ANFB. However, the filter weight initialization drawback that the conventional parallel form ANFB inherently suffers from is significantly alleviated in the proposed ANFB. Equipped with the ModNG algorithm, the proposed ANFB yields considerably improved frequency estimation performance as compared to the conventional parallel form ANFB, for a wide range of frequencies and signal-to-noise ratios (SNR). Furthermore, preliminary steady-state analysis is provided for both the proposed and conventional ANFBs, which significantly enhances our understanding of their capabilities and features. Extensive case study simulations are conducted to demonstrate the superiority of the proposed ANFB over the conventional parallel form ANFB. The proposed ANFB is also applied to real noise and vibration signals to reveal its promising analytical and practical capabilities.

A non‐linear multi‐sensory adaptive noise canceller (ANC, MsANC) with both multi‐primary and multi‐reference channels is proposed for foetal electrocardiogram (FECG) extraction. The primary channels are connected by a linear combiner (LC) whose output serves as a primary signal for the whole MsANC. A finite impulse response (FIR) filter or a non‐linear filter [a Volterra filter, or an FIR filter plus a functional link artificial neural network (FLANN), or an FIR filter plus a generalised FLANN] is placed in each reference channel to approximate the linear and non‐linear mappings between the chest maternal ECG (ANC reference signal) and the abdominal ECG (ANC primary signal). The LC connecting the primary channels is updated by a constrained recursive least square algorithm, while the linear and non‐linear filters placed in reference channels are updated in a least mean square sense. Two real datasets derived from the Physionet non‐invasive FECG database as well as the DaISy database are used to show the effectiveness of the proposed MsANC. Experimental results have revealed that the proposed MsANC results in considerable performance improvement as the number of primary channels is increased in comparison with existing ANCs with a single primary channel.

Online secondary-path modeling (SPM) based on the use of random auxiliary noise is a very promising and attractive technique for implementing active noise control (ANC). In this paper, a new strategy is proposed for the online SPM of narrowband ANC (NANC). A bandpass filter bank consisting of IIR notch filters is applied to the residual noise to separate the uncancelled sinusoidal noise from other broadband noises due to the injected auxiliary noise and an additive noise. The extracted sinusoidal noise is used simultaneously to scale the auxiliary noise, to clean the desired signal for the SPM subsystem, and to update the controller. The benefits of this strategy are twofold. It can effectively reduce the contribution of injected auxiliary noise to the residual noise power. Meanwhile, it can also considerably raise the convergence rate of overall system as well as the accuracy of online SPM. Preliminary analysis is also provided to support the proposed strategy. Extensive simulations with synthetic/real secondary paths and noises are performed to demonstrate the superiority of the proposed SPM strategy over the conventional techniques.

In this reply, we first provide a MATLAB program used in our simulations and clarify our claim regarding the occurrence of the so-called “firework” noise in the broadband active noise control (BANC) system. Next, we present some simulation results of the BANC using the FXLMS and the normalized FXLMS (NFXLMS) algorithms in the presence of a real noise signal generated by a large-scale rotating machine. The real noise is a mixture of several low-frequency sinusoids and a wideband noise component. The existence of “firework” noise is again confirmed in the BANC system when it is made to converge relatively fast.

In this paper, a variable step-size filtered-x LMS (VSS-FXLMS) algorithm is proposed for a typical narrowband active noise control system. The new algorithm converges much faster than the conventional FXLMS algorithm does, and indicates a convergence rate quite similar to that of the filtered-x recursive least square (FXRLS) algorithm in stationary noise environments. It also considerably outperforms these two existing algorithms in nonstationary situations. The proposed algorithm requires some more computations as compared with the FXLMS algorithm; however, its computational complexity is significantly less than that of the FXRLS algorithm. Numerous simulations for stationary and nonstationary scenarios are conducted to demonstrate the superior performance of the proposed VSS-FXLMS algorithm as compared with the FXLMS and the FXRLS algorithms.

In an active noise control (ANC) system, frequencies of the reference cosine and sine waves generated according to the online sensor information may be different from those of the primary noise to be suppressed, if the sensor contains error due to fatigue, aging etc. This difference, called frequency mismatch (FM), will severely deteriorate the performance of the system even though it is quite small. In this paper, three adaptive schemes are introduced for a feedforward hybrid ANC system, which are capable of effectively reducing the FM and thus making the system robust and immune against sensor error. Extensive simulations are conducted to confirm the effectiveness of the proposed adaptive schemes as well as the performance of the modified system.

This paper proposes a new order reduction approach for Roesser state-space model based on elementary operations, by inversely applying the basic idea adopted in the well-known elementary operation approaches to the Roesser state-space model realization of multidimensional (n-D) systems. It will be shown first that the order reduction problem can be formulated into an elementary operation problem of an initial n-D polynomial matrix constructed by the coefficient matrices of the given Roesser state-space model. Based on this formulation, a basic order reduction procedure is presented and examples are given to illustrate the details as well as the effectiveness of the proposed procedure.

Performance of the conventional broadband active noise control (ANC) system may degrade severely if its primary and reference noise signals contain both wideband and narrowband components simultaneously. In this letter, we propose a new feedforward hybrid ANC system capable of reducing such primary noise signals. First, typical simulation results are provided to show the performance deterioration of the conventional system in the presence of mixture of wideband and narrowband components. Next, a new hybrid ANC system is proposed to tackle the problem. The new system consists of three subsystems, i.e., a sinusoidal noise canceller, a broadband and a narrowband ANC subsystem, which work in harmony. Extensive simulations are conducted to demonstrate the effectiveness of the proposed system.

A new narrowband ANC system structure is proposed which requires only two reference signal filtering (x-filtering) blocks regardless of the number of targeted frequencies. The reference cosine or sine waves are combined, respectively, to form an input to an x-filtering block. The output of each x-filtering block is decomposed into filtered-x cosine or sine waves by a special bandpass filter bank. In this way, the computational cost of the system may be significantly reduced. Analysis of the new system is then provided and discussed in some detail. Analytical results reveal that the proposed system performs quite the same as its counterpart does while requiring considerably fewer multiplications. Modification to the proposed structure is also made to cope with the frequency mismatch (FM) in real-life applications. Extensive simulations are conducted to demonstrate the effectiveness of the proposed system and its modified version, and as well as to confirm the validity of analysis.

Online secondary-path modeling of active noise control (ANC) systems may be effectively implemented by injecting an auxiliary white noise whose magnitude is scaled by a function of residual noise signal. In this paper, a filtered-X LMS (FXLMS) based narrowband ANC system is analyzed, whose online secondary-path modeling is based on the use of an auxiliary white noise scaled by one-sample-delayed residual noise signal. Difference equations governing the dynamics of the entire system and closed-form expressions for steady-state mean-square errors (MSE) as well as the residual noise power are derived and discussed in detail. Extensive simulations are conducted to confirm the validity of the analytical findings.

In a typical conventional narrowband ANC system, the discrete Fourier coefficients (DFC) for each frequency are estimated by a linear combiner. Each reference (cosine or sine) wave has to be filtered by an estimate of the secondary-path before it is fed to the LMS algorithm. We call this part x-filtering block. The number of x-filtering blocks is 2q where q is the number of targeted frequencies. For larger q and/or higher order (̂M) of estimated FIR-type secondary-path, the computational cost due to x-filtering may become a bottle-neck in real system implementation. Here, we propose a new narrowband ANC system structure which requires only two (2) x-filtering blocks regardless of q. All the cosine waves (or sine waves) are combined as an input to a x-filtering block. The output of this block is decomposed by an efficient bandpass filter bank into filtered-x cosine or sine waves for the FXLMS that follows. The computational cost of the new system is significantly reduced especially for large q and/or ̂M. The new structure is also modified to cope with the frequency mismatch (FM). Simulations demonstrate that the new systems present performance which is very similar to that of the conventional system, but enjoy great advantages in system implementation.

Noise signals generated by rotating machines such as diesel engines, cutting machines, fans, etc., may be modeled as noisy sinusoidal signals which can be successfully suppressed by narrowband active noise control (ANC) systems. In this paper, statistical performance of such a conventional filtered-x LMS (FXLMS)-based narrowband ANC system is investigated in detail. First, difference equations governing the dynamics of the system are derived in terms of convergence of the mean and mean squared estimation errors for the discrete Fourier coefficients (DFCs) of the secondary source. Steady-state expressions for DFC estimation mean square error (MSE) as well as the residual noise power are then developed in closed forms. A stability bound for the FXLMS in the mean sense is also derived. Extensive simulations of various scenarios are performed to demonstrate the validity of the analytical findings.

Rotating machines such as diesel engines, cutting machines, fans, etc. generate sinusoidal noise signals that may be successfully reduced by narrowband active noise control (ANC) systems. In this paper, performance analysis of such a typical filtered-X LMS (FXLMS) based narrowband ANC system equipped with an online secondary path modeling subsystem is conducted in detail. First, difference equations governing the dynamics of the FXLMS for secondary source synthesis and the LMS for secondary path estimation are derived in terms of convergence of both mean and mean square. Steady-state expressions for mean square error (MSE) as well as the remaining noise power are then developed in closed forms. Extensive simulations are performed to demonstrate the validity of the analytical results.

The actual sound environment system exhibits various types of linear and non-linear characteristics, and it often contains an unknown structure. Furthermore, the observations in the sound environment often contain fuzziness due to several causes. In this study, a method for estimating the specific signal for acoustic environment systems with unknown structure and fuzzy observation is proposed by introducing a fuzzy probability theory and a system model of conditional probability type. The effectiveness of the proposed theoretical method is confirmed by applying it to the actual problem of psychological evaluation for the sound environment.

Human facial expression recognition (FER) has attracted much attention in recent years because of its importance in realizing highly intelligent human-machine interfaces. In this paper, we propose a new FER technique that utilizes the 2-D DCT of full size facial images and a decision tree with feedforward neural network (NN) based nodes. The first NN-based node of the decision tree is designated to separate one group of facial expressions with members "smile" and "surprise" from another group that contains "anger" and "sadness". This node can reduce the confusion between the category members of the two groups. Two NN-based nodes that follow the first node are established for each group to separate their two members. As a result, the original recognition problem with four categories is divided into three subproblems, each having only two members to distinguish. This work is the first trial to use NN, decision tree and 2-D DCT simultaneously within a single recognition task. To demonstrate the capability of the proposed recognition technique, we use two databases, including a recently constructed one, which contain 2-D front face images of 60 men and 60 women, respectively. Experimental results reveal that the new technique outperforms, on the whole, the simple vector matching and K-means based vector matching techniques and two recently developed methods using fixed-size and constructive neural networks. The mean recognition rates of the new technique have been found as high as 97.5% and 93.

Narrowband active noise control (ANC) systems have many real-life applications where the noise signals generated by rotating machines are modeled as sinusoidal signals in additive noise. However, when the timing signal sensor, such as a tachometer used to identify the signal frequencies, and the cosine wave generator contain errors, the frequencies of reference sinusoids fed to each ANC channel will then be different from the real primary noise signal frequencies. This difference is referred to as frequency mismatch (FM). In this paper, through extensive simulations, we first demonstrate that the performance capabilities of a conventional parallel form narrowband ANC system using the filtered-x LMS (FxLMS) algorithm degrades significantly even for an FM as small as 1 %. Convergence of the algorithm in the mean sense is also analytically investigated for a better understanding of its performance degradation. Next, we propose a new narrowband ANC system that successfully compensates for the performance degradations due to the FM. The amplitude/phase adjustment of reference sinusoids and the FM mitigations in the proposed system are performed simultaneously in a harmonic fashion such that the influence of the FM can be removed almost completely. Simulations as well as application to a real noise signal are provided to demonstrate the effectiveness of the proposed new system

The purpose of this paper is to establish a new constructive procedure for realization of 2D systems by Fornasini-Marchesini second (F-MII) local state-space model which can further reduce the order of the resultant realization to about half of that obtained by the procedure given recently by the authors. Nontrivial illustrative examples are given.

Facial expression recognition plays a vital role in realizing a highly intelligent human-machine interface, and has recently attracted much attention. In this paper, we propose a new facial expression recognition method that utilizes the 2D DCT, k-means algorithm and vector matching. This technique is based on two main intuitive ideas: (i) complicated facial expression categories such as "anger" and "sadness", may be divided into several subcategories with different subfeature spaces where the recognition task can be performed with higher accuracy and (ii) the k-means algorithm may be used to cluster these subcategories. A new image database with five facial expressions (neutral, smile, anger, sadness, surprise) of 60 women was constructed using a computationally efficient projection-based technique. Experimental results using the new database and an existing one (60 men) reveal that the new technique outperforms the standard vector matching technique and two recently developed methods using fixed-size and constructive one-hidden-layer neural networks. The mean recognition rate can be as high as 95% for the two databases.