Digital adaptive controllers are widely used for feedforward active noise control, especially in headphones. In such applications, the secondary path delay, including the sampling and reconstruction effects, must be shorter than the primary path delay to maintain good broadband performance. A mixed analog and digital adaptive feedforward controller is developed to eliminate the added delay of the sampling and reconstruction. The analog controller is based on a state-filtered adaptive linear combiner, while the digital one uses an adaptive finite-impulse-response filter. It is shown that both filters can be adapted using the normalized filtered-reference LMS algorithm but with different secondary path models. A method to design the analog state-filter based on Padé’s approximation is described. The performance of the proposed controller with two analog states, the direct feedthrough and a 0.3 milliseconds delay, is assessed and compared to the separate analog or digital controllers in a controlled environment. The results highlight that adding the analog delay improves the digital controller performance by about 5 dB in this application, regardless of the primary noise direction.

It is often difficult to position microphones at the ear position of listeners to directly monitor the perceived sound, in local active sound control systems for example. The pressure at these positions can be estimated using virtual sensing with an array of remote monitoring microphones, however, if some assumptions are made about the sound field. In active control, the sound field due to the secondary sources can be reasonably easily accounted for but the primary sound field, which is to be controlled, will in general be due to a number of potentially correlated primary sources, whose positions are unknown and may vary in time. The virtual sensing in this application thus needs to be robust to the properties of the primary sound field, both in the choice of the remote monitoring microphone positions and in the design of the filters used to process these to estimate the pressure at the desired position. If the controller is feedforward, the causality of these filters may also be relaxed if the adaptive algorithm is designed to minimise a delayed virtual error signal. This paper describes examples of such robust design, particularly applied to the local active control of road noise in vehicles.

Automated Border Control (ABC) in airports and land crossings utilize automated technology to verify passenger identity claims. Accuracy, interaction stability, user error, and the need for a harmonized approach to implementation are required. Two models proposed in this paper establish a global path through ABC processes. The first, the generic model, maps separately the enrolment and verification phases of an ABC scenario. This allows a standardization of the process and an exploration of variances and similarities between configurations across implementations. The second, the identity claim process, decomposes the verification phase of the generic model to an enhanced resolution of ABC implementations. Harnessing a human-biometric sensor interaction framework allows the identification and quantification of errors within the system's use, attributing these errors to either system performance or human interaction. Data from a live operational scenario are used to analyze behaviors, which aid in establishing what effect these have on system performance. Utilizing the proposed method will aid already established methods in improving the performance assessment of a system. Through analyzing interactions and possible behavioral scenarios from the live trial, it was observed that 30.96% of interactions included some major user error. Future development using our proposed framework will see technological advances for biometric systems that are able to categorize interaction errors and feedback appropriately.

The reasons for hearing loss are complex and currently the mechanics are not entirely clear. Outer hair cell (OHC) loss is believed to play an important role. Experimental observations shown that damage on OHCs due to ototoxic acid starts from the outermost row to the innermost row, whereas, loss of OHCs due to intense noise exposure occurs from the innermost row to the outermost row. Inspired by these experiments, this study employs the finite element method to develop a detailed model of a slice of the human cochlea including cochlear fine structures. OHC motility is implemented by applying forces at the two ends of the OHCs in response to stereocilia deflection, which are believed to be a key process in cochlear amplification. In this way, the effects of a loss of OHCs due to either intense noise exposure or ototoxic acid can be studied by suppressing forces on individual OHCs. Change of cochlear mechanical amplification and vibration patterns inside the organ of Corti due to different hearing loss mechanisms can thus be predicted.

This paper describes a time domain design method for calculating the coefficients of FIR filters used to drive a loudspeaker array for personal audio. A motivating application is to boost the television audio in a certain spatial region with the aim of increasing the speech intelligibility of the hearing impaired. As the array is of small size, superdirective beamforming is applied to increase the low and mid frequency directional performance of the radiator. The filters for such arrays have previously been designed one frequency at a time, leading to non-causal filters that require a modeling delay for real time implementation. By posing the filter optimization in the time domain, the filter responses can be causally constrained, and the optimization is performed once for all frequencies. This paper considers the performance of such filters by carrying out off-line simulations, firstly using the impulse responses of point sources in the free field, and then with the measured responses of a loudspeaker array in an anechoic chamber. The simulation results show how the time domain optimization allows the creation of filters with either a low order or a low modeling delay.

In the above paper Chang and Kuo show that the convergence of parallel narrowband active noise control systems is limited by the interaction between the multiple narrowband adaptive filters. To overcome this problem they propose a new algorithm that provides separate error signals to each of the narrowband adaptive filters, generated by filtering the error signal using bandpass filters tuned to the control frequency of each of the adaptive filters. Although these bandpass filters do not introduce an additional phase shift at the control frequency they do introduce an additional group delay into the secondary path. It is shown in this correspondence that although introducing these bandpass filters allows an increase in the step size it may also limit the convergence speed compared to the conventional algorithm.

The SIFEM project targets the development of an infrastructure in order to semantically link open source tools and libraries with existing data as well as new knowledge towards the multi-scale finite element modelling of the inner-ear. The SIFEM system is designed based on an open architecture schema that consists of a set of tools and subsystems in order to develop robust multi-scale models. The project mainly delivers: (i) tools for finite elements modelling, (ii) cochlea reconstruction tool and (iii) 3D inner ear models visualization tool. The main scientific results contribute to the knowledge of alterations associated to diverse cochlear disorders and could lead, in long-term, to personalized healthcare. The overview of the SIFEM platform and its architecture is presented in this paper.

Biosimulation researchers use a variety of models, tools and languages for capturing and processing different aspects of biological processes. However, current modeling methods do not capture the underlying semantics of the biosimulation models sufficiently to support building, reusing, composing and merging complex biosimulation models originating from diverse experiments. In this paper, we propose an ontology based and multi-layered biosimulation model to facilitate researchers to share, integrate and collaborate their knowledge bases at Web scale. In particular, we investigate the semantic biosimulation model under the context of the multi-scale finite element (FE) modelling of the inner-ear. The proposed ontology-based biosimulation model will provide a homogenized and standardized access to the shared, semantically integrated and harmonized datasets for clinical data (histological data, micro-CT images of the cochlea, pathological data) and inner ear FE simulation models. The work presented in this paper is analyzed and designed as part of the SIFEM EU project.

As well as being able to reproduce sound in one region of space, it would be useful to reduce the level of reproduced sound in other spatial regions, with a “personal audio” system. For mobile devices this is motivated by issues of privacy for the user and the need to reduce annoyance for other people nearby. Such personal audio systems can be realized with arrays of loudspeakers that become superdirectional at low frequencies, when the array dimensions are small compared with the acoustic wavelength. The design of the array then becomes a compromise between performance and array effort, defined as the sum of mean squared driving signals. Various methods of formulating this tradeoff as a regularization problem have been suggested and the connection between these formulations is discussed. Large array efforts are due to strongly self-cancelling multipole arrays. A concern is then the robustness of such an array to variations in the acoustic environment and driver sensitivity and position. The design of an array that is robust to these uncertainties then leads to a generalization of regularization.

An emerging approach to the problem of identity theft is represented by the adoption of biometric authentication systems. Such systems however present several challenges, related to privacy, reliability and security of the biometric data. Inter-opera

Three novel adaptive algorithms are proposed for use in active sound-profiling, all based on the filtered-X least mean square (FXLMS) approach. The algorithms are analyzed for their stability properties and control effort load, and are compared with the currently used adaptive noise equalizer least mean square (ANE-LMS) algorithm. It is found that the most suitable algorithm, both in simulations and experimentally, is the phase scheduled command FXLMS (PSC-FXLMS), which possesses both low control effort and good stability in the face of plant model misestimation, especially when using an automatic phase command law.

This paper accounts the testing protocols used at the author's university. It examines the experiences of one particular study in dynamic signature verification. The paper also outlines some additions to the current testing methodologies.

This paper investigates the problem of removing random impulse noise from a white signal of Gaussian distribution. A nonlinear polynomial filter is used, whose coefficients are optimised using an exact least squares method. The method relies on exploiting the differing probability distributions of the impulsive noise and the Gaussian signal. The paper then looks at the effect of both the polynomial order and the normalised spike amplitude on the mean squared error and signal to noise ratio. The results are compared to the results found using a simple clipping filter. The results show that the optimal filter gives a much improved performance over the simple clipping filter in reducing the mean square error.

A form of LMS algorithm for adaptive feedforward control is presented in which the physical plant response is preconditioned by the inverse of its minimum phase part. The overall response from the output of the control filter to the output of the error sensors is thus equal to the all-pass part of the plant response. Since this has a flat magnitude response, the convergence speed of the adaptive algorithm is not affected by resonances in the plant response, as it is for the normal filtered reference and filtered error LMS for example. Simulations are presented for a control system using a plant response measured from a loudspeaker to a microphone inside a car which support these observations.

Many listeners with medium-severe hearing loss present audiograms with high frequency loss descending profiles. These patients can resolve spectral cues normally for lower frequency signals but they often show less ability to use high frequency information. We have presented in this paper a new Discrete Exponential Transform (DET), its fundamentals, and the correspondingly algorithm designed to compensate this particular feature of the impairment. The preliminary results of the processed speech material in two impaired listeners suggests that DET could be implemented in digital hearing aids to be used in patients with severe and moderate-severe hearing loses.

Algorithms used for multichannel adaptive feedforward control, such as the multiple error LMS algorithm, require a model of the cancellation paths, which a control engineer would refer to as the plant, to update the signals driving the secondary sources. Uncertainties in this plant model or in the physical plant can lead to instability or suboptimal performance. This paper describes the effect of practically realistic uncertainties in both estimated and physical plant on the stability and performance of such a control algorithm. The system used here has 32 error sensors and 16 secondary sources and is designed to control a pure tone of 88 Hz. We consider the structured uncertainties due to changes in the error sensor locations, changes in the secondary source locations, and the excitation frequency. Results of preliminary experiments and numerical simulations show that these uncertainties have very different effects on the stability and performance of the control system. The singular value decomposition of the transfer matrix is used to discuss the characteristics of each type of plant error in the principal coordinates of the system.

This paper presents a new multichannel adaptive filtering algorithm for the active control of single frequency noise in acoustic systems. Most active control systems with multiple error sensors minimize the sum of the modulus squared output of these sensors. An adaptive algorithm is presented that minimizes an alternative cost function which, in the limit, is equal to the maximum of the modulus squared values of all the error sensors. The physical consequences of minimizing the maximum modulus squared output to achieve noise reduction are investigated. An analytical framework is developed that covers the steady state performance as well as the convergence properties. By means of simulations, the proposed algorithm has been applied to a linear model of a one-dimensional (1-D) acoustic system and compared with the classical least squares solutions. The proposed algorithm is also used in simulations of the control of the pressure measured at 32 microphone positions in a room using 16 loudspeakers, when the room is excited with an 88 Hz pure tone. The results of these simulations show that the observed convergence and steady-state properties agree well with the theoretical predictions. A comparison with the least squares solutions leads to the conclusion that the proposed algorithm leads to a more uniform acoustic field in the enclosure than the classical least squares algorithm.

This paper deals with multiple input multiple output systems for active control of acoustic signals. These systems are used when the acoustic field is complex and therefore a number of sensors are necessary to estimate the sound field and a number of sources to create the cancelling field. A steepest descent iterative algorithm is applied to minimise the p-norm of a vector composed by the output signals of a microphone array. The existing algorithms deal with the 2-norm of this vector. This paper describes a general framework that covers the existing systems and then it focuses on the /spl infin/-norm minimisation algorithm. The minimax algorithm based on the /spl infin/-norm minimises the output signal which has the greatest power. It is shown by means of simulations using measured data from a real room that the minimax algorithm leads to a more uniform final noise field than the existing algorithms.

A frequency domain implementation of the LMS algorithm has significant advantages. In broadband applications it is important to use the correct window function before Fourier transformation to obtain an unbiased estimation of the required cross correlation function and to eliminate wrap-around effects. In the frequency domain filtered-X LMS algorithm described in this paper, the control filter is updated in the frequency domain as a background task, while control filtering is performed in the time domain, to minimize the processing delays. The frequency domain algorithm showed better performance than the conventional time domain algorithm in simulations of single channel active control systems. The algorithm is also able to improve the convergence of multiple channel systems by compensating for the coupling between the control channels.

Normalizing the convergence coefficient of the block frequency-domain least mean square (LMS) algorithm in each frequency bin can improve the convergence rate, but in some applications can lead to a biased steady-state solution if the filter is constrained to be strictly causal. An algorithm is presented in which the spectral factors of the bin-normalized convergence coefficient are used before and after the causality constraint is applied in the adaptation algorithm, which converges rapidly to the optimal causal filter.

The algorithms used for adaptive feedforward control often require a model of the acoustic plant being controlled to update the signals driving the secondary sources. This paper describes the effect of practically realistic plant estimation errors on the stability and performance of such a control algorithm. We have considered the structured plant errors due to changes in the error sensors location, changes in the secondary sources location and drift in the excitation frequency. These errors have very different effects on the stability and performance of the control system. The characteristics of each types of plant error are examined by singular value decomposition of the transfer matrix.

Adaptive algorithms are presented for constrained multichannel feedforward control. They minimize the sum of squared error signals while limiting the value of either the sum of squared control signals (total effort) or the mean square value of each individual control signal (individual efforts). It is shown that the exact least squares solutions for these problems have a simple form, which can be approached with an adaptive algorithm. The properties of the resulting algorithm are illustrated by simulation.

A consistent framework is presented for the calculation of the optimal performance of feedforward and feedback control systems in attenuating random disturbances. In both cases, the optimization problem is transformed into a quadratic form using an internal model of one part of the physical system under control. The resulting architecture for the feedback controller is known as internal model control (IMC) and is widely used in the H/sub /spl infin// control literature. With this controller architecture, the optimum performance of a multichannel feedback system can be readily calculated using the quadratic optimization techniques already developed in the sampled time domain for multichannel feedforward control. The robustness of the stability of such a feedback controller to changes in the plant response can be separately assessed using a generalization of the complementary sensitivity function, which has a particularly simple form when IMC is used. The stability robustness can be improved by incorporating various forms of effort weighting into the cost function being minimized, some of which are already used for adaptive feedforward controllers. By way of example, the performance is calculated of both feedforward and feedback controllers for the active attenuation of road noise in cars. The variation of performance with loop delay is calculated for both types of control, and it is found that in this example, the potential attenuation is greatest using feedback control but only if the loop delay is less than 1.5 ms.

Centralized feedforward techniques are often used in active control systems that simultaneously adjust the inputs to multiple secondary sources to minimize the sum of the squared outputs from multiple error sensors. If the number of transducers is large, such centralized control systems require considerable computational power and cabling. In this paper, the stability and performance of decentralized adaptive feedforward systems are considered, in which a number of smaller adaptive controllers are implemented independently, each driving subsets of all the secondary sources to minimize the sum of squared outputs of subsets of the error sensors. The limit of this philosophy is when each secondary source is adjusted to minimize the output of only one error sensor. A general theoretical analysis of such decentralized feedforward controllers is developed. It is also shown that a simple condition can be derived, using the Gerschgorin circle theorem, which provides a sufficient, but not necessary, condition for the stability of the overall system. Unfortunately, this condition appears to be rather conservative for larger systems. Examples of the application of this general analysis are presented for two and three channel active sound control systems operating in free space and a 32-channel sound control system operating in an enclosure. Provided the error sensors are closer to the secondary source used to control them than the other secondary sources, the stability of the control system is seen to be quite robust to decentralization. The steady-state performance is also similar to that of a fully coupled control system, although the transient response generally involves oscillatory modes, which sometimes decay only rather slowly.>

An algorithm for multichannel adaptive IIR (infinite impulse response) filtering is presented and applied to the active control of broadband random noise in a small reverberant room. Assuming complete knowledge of the primary noise, the theoretically optimal reductions of acoustic energy are initially calculated by means of a frequency-domain model. These results are contrasted with results of a causality constrained theoretical time-domain optimization which are then compared with experimental results, the latter two results showing good agreement. The experimental performances of adaptive multichannel FIR (finite impulse response) and IIR filters are then compared for a four-secondary-source, eight-error microphone active control system, and it is found that for the present application FIR filters are sufficient when the primary noise source is a loudspeaker. Some experiments are then presented with the primary noise field generated by a panel excited by a loudspeaker in an adjoining room. These results show that far better performances are provided by IIR and FIR filters when the primary source has a lightly damped dynamic behavior which the active controller must model.>

In active sound control, the sound field generated by the operation of a number of secondary actuators is adjusted to destructively interfere, to the greatest possible extent, with the sound field caused by the original, primary, source of sound. An example would be the adjustment of the inputs to structural actuators attached to a panel, also excited by a primary source of vibration, to minimize the acoustic power radiated. In practice the inputs to a number of actuators could be adjusted to minimize the sum of the squared signals from a greater number of sensors-an overdetermined control problem, for which adaptive algorithms have already been developed. An alternative control strategy is to adjust the actuators so that the signal from a single sensor, such as one measuring the net volume velocity of a panel, is driven to zero with the least control effort. Adaptive algorithms for such underdetermined control problems are discussed.>

The authors present exact descriptions of the behaviour of a class of adaptive filters with delayed coefficient adaption. This coefficient update delay is demonstrated to occur as a natural consequence of introducing compensation for nonminimum phase systems under the control of a least mean square based adaptive filter, as in the adaptive control of acoustic noise. A linear, time-invariant equivalent transfer function model of the adaptive system is developed, which can be used to study steady-state performance and the stability limits imposed by the coefficient update delay.>

The active control of harmonic sound using multiple secondary sources and multiple error microphones is formulated in an adaptive digital filtering framework. A stochastic gradient algorithm is presented to minimize the sum of the squares of the error microphone outputs by adapting the coefficients of an array of FIR (finite-impulse response) filters driving the secondary sources. The properties and implementation of the algorithm are discussed and some results are presented which demonstrate that the noise at the engine firing frequency inside an automobile can be reduced by 10-15 dB using a practical implementation of such an active sound control system, which had a response time of 0.1 s in this application.>

This paper considers the behaviour of dynamic systems under the control of the LMS algorithm, defining the limiting values of the plant's frequency response which yield a stable controller. The technique is extended to cover standard compensation strategies, including the "filtered-x" algorithm.

The problem is addressed of evaluating the maximum net reduction in acoustic power output produced by introducing a single point monopole secondary source to control the sound field radiated by a single point monopole primary source in a free field. The secondary source is constrained to act causally with respect to the primary source. The problem reduces to solving a Wiener-Hopf integral equation for the optimal realisable impulse response relating the secondary and primary source outputs. A solution is given which has a well-defined physical significance and demonstrates the need for the secondary source to act as a predictor in order that destructive interference of the primary field is possible at field points closer to the primary source than the secondary source.