Wiener Approach Research Articles

Deep learning for speech denoising with improved Wiener approach

Deep learning for speech denoising with improved Wiener approach

Advanced Control Algorithm for FADEC Systems in the Next Generation of Turbofan Engines to Minimize Emission Levels

New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO2, NOx, and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines.

Speech intelligibility enhancement: a hybrid wiener approach

Speech enhancement primarily focuses on improving the intelligibility and quality of the speech signal by using various algorithms and techniques. Processing of a speech signal refers to applying efficient mechanisms to reduce noise in the way of extracting the intended speech signal from the corrupted signal. Noise reduction techniques such as kalman filtering, spectral subtraction and adaptive wiener filtering etc. are used in different enhancement scenarios in speech processing. In the proposed method, the combination of wiener filter and Karhunen–Loeve Transform is used to remove noise and enhance the noisy speech signal. This paper presents the performance evaluation of the proposed hybrid algorithm by estimating Signal to Noise Ratio, Perceptual Evaluation of Speech Quality, Short-Time Objective Intelligibility and Extended STOI values. This algorithm has been implemented in varied noisy conditions and the results proved the fruitfulness of this method. Subjective listening evaluation is also done and both the objective and subjective results confirmed the significant improvement in speech intelligibility in the proposed method.

On the Crossroad of Artificial Intelligence: A Revisit to Alan Turing and Norbert Wiener.

To give a high-level summary to current approaches for implementing artificial intelligence (AI), we explain the key commonalities and major differences between Turing's approach and Wiener's approach in this perspective. Especially, the problems, successful achievements, limitations, and future research directions of existing approaches that follow Weiner's ideas are addressed, respectively, aiming to provide readers with a good start point and a roadmap. Some other related topics, for example, the role of human experts in developing AI, are also discussed to seek potential solutions for some existing difficulties.

On single-channel noise reduction with rank-deficient noise correlation matrix

The widely studied subspace and linear filtering methods for noise reduction require the noise correlation matrix to be invertible. In certain application scenarios, however, this matrix is either rank deficient or very ill conditioned, so this requirement cannot be fulfilled. In this paper, we investigate possible solutions to this important problem based on subspace techniques for single-channel time-domain noise reduction. The eigenvalue decomposition is applied to both the speech and noise correlation matrices to separate the null and nonnull subspaces. Then, a set of optimal and suboptimal filters are derived from the nullspace of the noise signal. Through simulations, we observe that the proposed filters are able to significantly reduce noise without introducing much distortion to the desired signal. In comparison with the conventional Wiener approach, the developed filters perform significantly better in improving both the signal-to-noise ratio (SNR) and the perceptual evaluation of speech quality (PESQ) score when the noise correlation matrix is rank deficient.

Krylov subspace-based model reduction for a class of bilinear descriptor systems

We consider model order reduction for bilinear descriptor systems using an interpolatory projection framework. Such nonlinear descriptor systems can be represented by a series of generalized linear descriptor systems (also called subsystems) by utilizing the Volterra–Wiener approach (Rugh, 1981). Standard projection techniques for bilinear systems utilize the generalized transfer functions of these subsystems to construct an interpolating approximation. However, the resulting reduced-order system may not match the polynomial parts of the generalized transfer functions. This may result in an unbounded error in terms of H2 or H∞ norms. In this paper, we derive an explicit expression for the polynomial part of each subsystem by assuming a special structure of the bilinear system which reduces to an index-1 linear descriptor system or differential algebraic equation (DAE) if the bilinear terms are zero. This allows us to propose an interpolatory technique for bilinear DAEs which not only achieves interpolation, but also retains the polynomial parts of the bilinear systems. The approach extends the interpolatory technique for index-1 linear DAEs (Beattie and Gugercin, 2009) to bilinear DAEs. Numerical examples are used to illustrate the theoretical results.

A Novel Self-Consistent Model Based Optimal Filter Design for the Improved Dynamic Performance of 3-phase PLLs for Phase Tracking Under Grid Imperfections

In the context of recent advancements in 3-phase phase-locked loop (PLL) structures to tackle grid imperfections, this paper attempts to shift focus towards dynamic response optimization for fast tracking of disturbed grids, as opposed to Wiener optimization, a trade-off between filtering characteristic and dynamic response. In this respect, an ingenious self-consistent model (SCM)-based approach is proposed which explores filter design in the presence of frequency shifts and phase jumps, and facilitates the analytical computation of unique loop filter parameters. Trial and error in filter parameter selection is inconvenient, but more importantly, even rigorous trials would be insufficient in qualifying the non-existence of a better design. Having eliminated trial and error, this novel technique limits transients to user specifications while fixing on an optimum damping ratio, to yield the best fit. The design methodology is applied to three existing 3-phase PLL structures modelled in MATLAB/Simulink, and the proposed method is further evaluated through extensive simulations and performance comparisons with the traditional Wiener approach. To enhance the understanding of model behaviour and the feasibility of practical implementation, comprehensive three-dimensional (3-D) lookup tables are presented. They enable the study of optimized filter parameter variations for a range of grid disturbances, and broaden the application to filter optimization in real-time. In the interest of the reader, this paper is structurally split into two parts. Part 1 covers the premise and theory that explicates the proposed SCM methodology. The detailed analysis and verification of the SCM are covered in Part 2.

Random walks of colloidal probes in viscoelastic materials.

To overcome limitations of using a single fixed time step in random walk simulations, such as those that rely on the classic Wiener approach, we have developed an algorithm for exploring random walks based on random temporal steps that are uniformly distributed in logarithmic time. This improvement enables us to generate random-walk trajectories of probe particles that span a highly extended dynamic range in time, thereby facilitating the exploration of probe motion in soft viscoelastic materials. By combining this faster approach with a Maxwell-Voigt model (MVM) of linear viscoelasticity, based on a slowly diffusing harmonically bound Brownian particle, we rapidly create trajectories of spherical probes in soft viscoelastic materials over more than 12 orders of magnitude in time. Appropriate windowing of these trajectories over different time intervals demonstrates that random walk for the MVM is neither self-similar nor self-affine, even if the viscoelastic material is isotropic. We extend this approach to spatially anisotropic viscoelastic materials, using binning to calculate the anisotropic mean square displacements and creep compliances along different orthogonal directions. The elimination of a fixed time step in simulations of random processes, including random walks, opens up interesting possibilities for modeling dynamics and response over a highly extended temporal dynamic range.

Filtering of infinite sets of stochastic signals: An approach based on interpolation techniques

We propose an approach to the filtering of infinite sets of stochastic signals, K Y and K X . The known Wiener-type approach cannot be applied to infinite sets of signals. Even in the case when K Y and K X are finite sets, the computational work associated with the Wiener approach becomes unreasonably hard. To avoid such difficulties, a new theory is studied. The problem addressed is as follows. Given two infinite sets of stochastic signals, K Y and K X , find a single filter F : K Y → K X that estimates signals from K Y with a controlled associated error. Our approach is based on exploiting a signal interpolation idea. The proposed filter F is represented in the form of a sum of p terms, F ( y ) = ∑ j = 1 p T j R j Q j ( y ) . Each term is derived from three operations presented by matrices, Q i , R i and T i with i=1,…, p. Each operation is a special stage of the filtering aimed at facilitating the associated numerical work. In particular, Q 1 , … , Q p are used to transform an observable signal y ∈ K Y to p different signals. Matrices R 1,…, R p reduce a set of related matrix equations to p independent equations. Their solution requires much less computational effort than would be required with the full set of matrix equations. Matrices T i ,…, T p are determined from interpolation conditions. We show that the proposed filter is asymptotically optimal. Moreover, the filter model is determined in terms of pseudo-inverse matrices and, therefore, it always exists.

The origins of British cybernetics: the Ratio Club

PurposeThe purpose of this paper is to describe the origins, members, activities, and influence of the Ratio Club, a British cybernetic dining club that met between 1949 and 1958. Although its membership included some of the best known British cyberneticists, such as Grey Walter and Ross Ashby, along with pioneering scientists such as Alan Turing, the club is poorly documented, and its significance is difficult to establish from published sources.Design/methodology/approachThe approach involved the consultation and analysis of unpublished material in both private and public archives in the UK and the USA, coupled with interviews with surviving members, guests, and contemporaries.FindingsThe Ratio Club grew out of a distinctively British strand of cybernetic activity that was mainly fuelled by the deployment of biologists to engineering activities during the Second World War. It was also strongly influenced by the approach of the psychologist Kenneth Craik. Although members were keenly aware of contemporary American developments, such as Wiener's approach to the mathematics of control, and the psychological and sociological concerns of the Macy Conference, the emphasis of the club was on the application of cybernetic ideas and information theory to biology and the brain. In contrast to the wide influence the later Macy conferences exercised through their published transcripts, the Ratio Club influenced its core disciplines though its members, several of whom became prominent and effective advocates of the cybernetic approach.Originality/valueThis is the first journal paper to give an authoritative, detailed, and accurate account of the club's origins, activities, and importance.

Adaptive Algorithms for Enhancement of Speech Subject to a High-Level Noise

AbstractThere are many industrial environments which are exposed to a high-level noise, sometimes much higher than the level of speech. Verbal communication is then practically unfeasible. In order to increase the speech intelligibility, appropriate speech enhancement algorithms can be used. It is impossible to filter off the noise completely from the acquired signal by using a conventional filter, because of two reasons. First, the speech and the noise frequency contents are overlapping. Second, the noise properties are subject to change. The adaptive realisation of the Wiener-based approach can be, however, applied. Two structures are possible. One is the line enhancer, where the predictive realisation of the Wiener approach is used. The benefit of using this structure it that it does not require additional apparatus. The second structure takes advantage of the high level of noise. Under such condition, placing another microphone, even close to the primary one, can provide a reference signal well correlated with the noise disturbing the speech and lacking the information about the speech. Then, the classical Wiener filter can be used, to produce an estimate of the noise based on the reference signal. That noise estimate can be then subtracted from the disturbed speech. Both algorithms are verified, based on the data obtained from the real industrial environment. For laboratory experiments the G. R. A. S. artificial head and two microphones, one at back side of an earplug and another at the mouth are used.

When self-consistency makes a difference

Compound semiconductor power RF and microwave device modeling requires, in many cases, the use of self- consistent electrothermal equivalent circuits. The slow thermal dynamics and the thermal nonlinearity should be accurately included in the model; otherwise, some response features subtly related to the detailed frequency behavior of the slow thermal dynamics would be inaccurately reproduced or completely distorted. Two examples have been shown, concerning current collapse in HBTs and modeling of IMPs in GaN HEMTs. Accurate thermal modeling can be made compatible with circuit-oriented CAD tools through a proper choice of system-level approximation; in the discussion presented it exploited a Wiener approach, but of course the strategy should be tailored to the specific problem under consideration.

Iterative Desensitisation of Image Restoration Filters under Wrong PSF and Noise Estimates

The restoration achieved on the basis of a Wiener scheme is an optimum since the restoration filter is the outcome of a minimisation process. Moreover, the Wiener restoration approach requires the estimation of some parameters related to the original image and the noise, as well as knowledge about the PSF function. However, in a real restoration problem, we may not possess accurate values of these parameters, making results relatively far from the desired optimum. Indeed, a desensitisation process is required to decrease this dependency on the parameter errors of the restoration filter. In this paper, we present an iterative method to reduce the sensitivity of a general restoration scheme (but specified to the Wiener filter) with regards to wrong estimates of the said parameters. Within the Fourier transform domain, a sensitivity analysis is tackled in depth with the purpose of defining a number of iterations for each frequency element, which leads to the aimed desensitisation regardless of the errors on estimates. Experimental computations using meaningful values of parameters are addressed. The proposed technique effectively achieves better results than those obtained when using the same wrong estimates in the Wiener approach, as well as verified on an SAR restoration.

Adaptive removal of azimuth ambiguities in SAR images

We introduce an innovative algorithm that is capable of suppression of strong azimuth ambiguities in single-look complex (SLC) synthetic aperture radar images, to be used both for detected products and for interferometric surveys. The algorithm exploits a band-pass filter to select that portion of the azimuth spectrum that is less influenced by aliasing, the one that corresponds to the nulls in the replicated azimuth antenna pattern. The selectivity of this filter adapts locally depending on the estimated ambiguity intensity: the filter is more selective in the presence of strong ambiguities and becomes all-pass in absence of ambiguities. The design of such filter frames in the Wiener approach with two different normalization options, depending on the use of the SLC image, either for getting multilook averaged detected products or for interferometric applications. Preliminary results achieved by processing ENVISAT Advanced Synthetic Aperture Radar sensor data are provided.

Blind Search for Optimal Wiener Equalizers Using an Artificial Immune Network Model

This work proposes a framework to determine the optimal Wiener equalizer by using an artificial immune network model together with the constant modulus (CM) cost function. This study was primarily motivated by recent theoretical results concerning the CM criterion and its relation to the Wiener approach. The proposed immune-based technique was tested under different channel models and filter orders, and benchmarked against a procedure using a genetic algorithm with niching. The results demonstrated that the proposed strategy has a clear superiority when compared with the more traditional technique. The proposed algorithm presents interesting features from the perspective of multimodal search, being capable of determining the optimal Wiener equalizer in most runs for all tested channels.

Application of experimental-based modeling to laser cladding

This article addresses experimental-based modeling of the powder injection laser cladding process. An accurate model for the process is essential for controller design and process automation. There are few dynamic and static models for the process, however, due to the limitations and intensive numerical calculations, their application to real-time process control are not practical. Two model structures, the Hammerstein–Wiener model and Elman recurrent neural network are implemented to identify the laser cladding dynamic model. The type of experiments and collected data are explained, and model predictions for different and unseen data are compared with experimental results. It is shown that the Hammerstein–Wiener approach can more accurately describe the transient response of the process. Results are promising and show that experimental-based models can effectively be used in laser cladding processes.

Process Identification Based on Wiener Constrained Models

Modelling of dynamic non-linear processes using the Wiener approach is considered. Models are constructed based on sampled measurements from the process, as well as a priori information of the characteristics of the process. Prior information is expressed by a set of constraints, which leads to an optimisation problem of parameter estimation under constraints. Modelling of a two-tank system Illustrates the performance of the approach

Non-linear process modelling based on a Wiener approach

The identification of multiple-input single-output Wiener models is considered in this paper. The non-linear memoryless part is described by a parametrized steady state model. In this paper two representations for the linear dynamic part that preserve the unit steady state gain are discussed and compared: finite step response and transfer function with a feedback polynomial. The steps necessary for estimating the parameters of the Wiener model are presented. In the simulation examples with data from a pneumatic valve model, distillation column model and a pilot pump-valve system, the performance of the approach is examined, and the use of various kinds of non-linear black-box and grey-box structure for the modelling of the static part is illustrated.

Comparison between ISRA and RLA algorithms. Use of a Wiener Filter based stopping criterion

This paper consists of two parts. In the rst one, we make a comparative analysis of the algo- rithms of Richardson-Lucy (RLA) and the Image Space Reconstruction Algorithm (ISRA), for image deconvolu- tion of astronomical images. These iterative algorithms keep the reconstructed image non-negative while max- imizing the likelihood for a Poisson Process (RLA) or a Gaussian process (ISRA). Their comparison is made easier when these algorithms are rewritten as descent algorithms; the additive forms evidence the role of the variances of the noise and allow a better understanding of these algorithms. A numerical illustration is performed from simulated images. In practice, the results obtained by the two algorithms appeared to be very similar, independently of the statistics of the noise. In the second part of the paper, we propose a new objective-stopping technique that makes use of a com- parison of the results of these algorithms with that of the Wiener lter. The comparison is made in the Fourier plane, computing the Euclidean distances between mod- ules of the spatial frequencies components of the images. We propose then to use the results of ISRA and RLA at the iteration number corresponding to the minimum of that distance. The technique is checked in a numerical simulation, for which the optimal iteration numbers can be easily determined. A good agreement is obtained between best ISRA and RLA results and the Wiener lter, in particular for rather noisy images. However ISRA and RLA produce very high frequency components, outside the cut-o frequency of the instrument; limiting the iteration number alone cannot al- low a perfect agreement with the Wiener approach, stress- ing the need of an explicit regularization for astronomical applications.

Decomposition of neural systems with nonlinear feedback using stimulus–response data

The need often arises in many modeling studies of physiological systems with feedback, for separate characterization of the feedthrough and feedback components using stimulus–response data from the entire system. For instance, the hippocampal formation consists of multiple feedback connections which are difficult to identify experimentally. This paper investigates the application of adaptive estimation techniques in the context of the Volterra–Wiener approach to decompose unobservable subsystems from the overall feedback system data. Computer simulation studies have demonstrated its effectiveness over the traditional approach which employs nonlinear systems analysis in the frequency domain. This approach can be used to indirectly characterize the unobservable feedback basket cells in the hippocampus utilizing experimental stimulus–response data from dentate granule cells.