Evolving multimedia systems are increasingly being adopted in virtual reality and gaming applications. Such systems emphasize immersion to engage users by bridging the gap between real and virtual content. In this context, visual and acoustic stimuli are the two key media that dictate such immersion. While visual 3D rendering is advancing rapidly, the same is not true for audio, where most research is limited to the reconstruction of the room impulse response (RIR) using omnidirectional audio or, at best, binaural. Such methods do not adequately account for the directions and orientations of the acoustic signals with respect to either the source or the listener, thereby compromising immersion quality. In this work, we explore the effect of adding such "directionality" to the training data to improve the estimation of the room’s acoustic parameters. A more accurate set of such parameters implies in fact a more realistic predicted RIR, leading to a more immersive experience of the acoustic scene. Specifically, we propose a novel framework driven by a suitable loss function to account for directionality in ambisonic microphones, and novel variants of loss functions for both omnidirectional and ambisonic cases. We also propose to account for microphone characteristics and their contribution to the predicted RIRs. Experiments were performed using two datasets of real recordings and the results established the efficacy of the proposed methods

This paper studies the use of Blind Source Separation (BSS) as a replacement for Coded Random Access in diversity ALOHA massive machine-type communications. We propose a scheme for efficiently implementing BSS within the ALOHA framework, while also determining the number of active users in each slot. We experimentally evaluate the achievable performance and highlight the factors that influence the number of detectable users.

The paper proposes a novel Electrochemical Impedance Spectroscopy methodology that takes into account the nonlinearties present in the battery. For this purpose, the current-voltage relationship of the cell is modeled in discrete domain as a Volterra filter and the linear part of the nonlinear model is efficiently estimated using Orthogonal Periodic Sequences. Preliminary experimental results involving six lithium-ion cells show the soundness of the proposed approach also in comparison with the classical method.

This manuscript describes a methodology for measuring the first-order Volterra kernel of a discrete-time nonlinear system, that is, the impulse response for small signal amplitudes of the nonlinear system. In the proposed approach, multiple linear identifications are performed using the same excitation signal multiplied by different gains, and the first-order Volterra kernel is obtained from a polynomial interpolation of the measured values. Any linear identification method proposed in the literature can be used with this approach. The proposed approach is a multiple variance (MV) method that, in contrast to all other MV methods, aims to estimate one of the kernels of the Volterra model, the first-order kernel, with high precision. The manuscript discusses the proposed methodology, determines the mean square deviation (MSD) due to noise of the measured coefficients, and the value of the optimal gains that minimize the MSD. Remarkably, it is shown in the manuscript that the optimal gains assume only a reduced set of values that depends on the order of nonlinearity. The optimal number of measurements is also determined. The conditions under which the proposed methodology is more convenient than the classical linear methods are discussed. The experimental results demonstrate the proposed methodology and its strengths.

An open problem in room impulse response (RIR) measurement is the effect of nonlinearities, especially those with memory, present in the measurement system, specifically in the power amplifier and in the loudspeaker. The nonlinearities can corrupt the measurement introducing artifacts. The paper discusses a RIR measurement method that is robust towards these nonlinearities. The proposed methodology allows measuring the RIR using the cross-correlation method, i.e., computing the cross-correlation between the output signal and an appropriate sequence. In contrast to other cross-correlation based methods, the proposed approach directly estimates the first-order kernel of the Volterra filter modeling the measurement systems, i.e., the system impulse response for small signals. The proposed approach exploits the concepts of orthogonal periodic sequences, recently proposed in the literature. The input signal can be any periodic persistently exciting sequence and can also be a quantized sequence. Measurements performed both on an emulated scenario and in real environments illustrate the validity of the approach and compare it with other competing RIR measurement methods.

The paper introduces a novel family of deterministic signals, the orthogonal periodic sequences (OPSs), for the identification of functional link polynomial (FLiP) filters. The novel sequences share many of the characteristics of the perfect periodic sequences (PPSs). As the PPSs, they allow the perfect identification of a FLiP filter on a finite time interval with the cross-correlation method. In contrast to the PPSs, OPSs can identify also non-orthogonal FLiP filters, as the Volterra filters. With OPSs, the input sequence can have any persistently exciting distribution and can also be a quantized sequence. OPSs can often identify FLiP filters with a sequence period and a computational complexity much smaller than that of PPSs. Several results are reported to show the effectiveness of the proposed sequences identifying a real nonlinear audio system.

The paper introduces a novel class of complex nonlinear filters, the complex functional link polynomial (CFLiP) filters. These filters present many interesting properties. They are a sub-class of linear-in-the-parameter nonlinear filters. They satisfy all the conditions of Stone-Weirstrass theorem and thus are universal approximators for causal, time-invariant, discrete-time, finite-memory, complex, continuous systems defined on a compact domain. The CFLiP basis functions separate the magnitude and phase of the input signal. Moreover, CFLiP filters include many families of nonlinear filters with orthogonal basis functions. It is shown in the experimental results that they are capable of modeling the nonlinearities of high power amplifiers of telecommunication systems with better accuracy than most of the filters currently used for this purpose.

The paper proposes a novel approach for measuring the room impulse response that is robust toward the nonlinearities affecting the power amplifier or the loudspeaker. The approach is implemented by modeling the acoustic path as a Legendre nonlinear filter and by measuring the first-order kernel using perfect periodic sequences and the cross-correlation method. Perfect sequences for Legendre filters are periodic sequences that guarantee the orthogonality of the Legendre basis functions over a period. They ensure the robustness of the first kernel measurement toward nonlinear distortions. The paper also explains how perfect periodic sequences for Legendre filters that are suitable for room impulse response identification can be developed. Experimental results involving both simulated and real environments illustrate the effectiveness and the robustness of the proposed approach.

This work is part of a project, SmartRoadSense, aimed to estimate the quality of the road surface exploiting the sensors of a mobile device anchored to the car cabin. Using the data of the triaxial accelerometer and the vehicle speed provided by GPS system, it has been shown that an estimate of the quality of road surface can be obtained. In order to improve this estimate, the paper studies the dependence of the mobile device vertical acceleration on the vehicle speed. Using a theoretical model and experimental results, it is shown how the average power of the vertical acceleration and the road roughness index estimated by SmartRoadSense are related to speed.

No abstract available.

This paper introduces a novel sub-class of linear-in-the-parameters nonlinear filters, the Legendre nonlinear filters. Their basis functions are polynomials, specifically, products of Legendre polynomial expansions of the input signal samples. Legendre nonlinear filters share many of the properties of the recently introduced classes of Fourier nonlinear filters and even mirror Fourier nonlinear filters, which are based on trigonometric basis functions. In fact, Legendre nonlinear filters are universal approximators for causal, time invariant, finite-memory, continuous, nonlinear systems and their basis functions are mutually orthogonal for white uniform input signals. In adaptive applications, gradient descent algorithms with fast convergence speed and efficient nonlinear system identification algorithms can be devised. Experimental results, showing the potentialities of Legendre nonlinear filters in comparison with other linear-in-the-parameters nonlinear filters, are presented and commented.

In this paper we consider the identification of a class of linear-in-the parameters nonlinear filters that has been recently introduced, the so-called even mirror Fourier nonlinear filters. We show that perfect periodic sequences can be derived for these filters. A periodic sequence is perfect for a nonlinear filter if all cross-correlations between two different basis functions, estimated over a period, are zero. By applying perfect periodic sequences as input signals to even mirror Fourier nonlinear filters, it is possible to model unknown nonlinear systems exploiting the cross-correlation method. Then, the most relevant basis functions, i.e., those that guarantee the most compact representation of the nonlinear system according to some information criterion, can be easily estimated. Experimental results on the identification of a real nonlinear system illustrate the effectiveness of the proposed approach.

In this paper, we introduce a novel class of nonlinear filters characterized by two interesting properties: (i) according to the Stone-Weierstrass approximation theorem, the proposed nonlinear filters are able to arbitrarily well approximate any discrete-time, time-invariant, causal, infinite-memory, continuous, nonlinear system; (ii) the filters are always stable according to the bounded-input-bounded-output criterion. This novel class includes as a subclass the finite-memory even mirror Fourier nonlinear filters, which have been recently introduced as an useful tool for modeling strong saturation nonlinearities.

In this study, the unconstrained linear combination of the outputs of even mirror Fourier non‐linear filters is considered. These filters are new members of the class of causal, shift‐invariant, finite‐memory and linear‐in‐the parameters non‐linear filters. Their name derives from the even symmetry of their trigonometric basis functions. Even mirror Fourier non‐linear filters are universal approximators for causal, time invariant, finite‐memory and continuous non‐linear systems. Moreover, their basis functions are mutually orthogonal for white uniform input signals in the interval [−1, +1]. The authors show in this study how to exploit these characteristics, in the framework of the unconstrained linear combination of non‐linear filters, for modelling unknown non‐linear systems. In particular, they show that the filters whose outputs are combined can be adapted avoiding the choice of the step sizes, by using a simple algorithm presented in this study. The analysis of the proposed structures is accompanied by a set of simulation results that confirm the good performance obtained in different situations.

In this paper, a novel sub-class of linear-in-the-parameters (LIP) nonlinear filters, formed by the so-called even mirror Fourier nonlinear (EMFN) filters, is presented. These filters are universal approximators for causal, time invariant, finite-memory, continuous nonlinear systems as the well-known Volterra filters. However, in contrast to Volterra filters, their basis functions are mutually orthogonal for white uniform input signals. Therefore, in adaptive applications, gradient descent algorithms with fast convergence speed and efficient nonlinear system identification algorithms can be devised. Preliminary results, showing the potentialities of EMFN filters in comparison with other LIP nonlinear filters, are presented and commented.

This paper deals with methods for multiple position room response equalization. Differently from a well-known technique working in the time domain and based on fuzzyc-means clustering, in the proposed approach most of the operations are performed in the frequency domain and, in particular, the fuzzyc-means clustering is applied to the room magnitude responses at different positions. It is shown that working in the frequency domain allows us to obtain equalization performances at least similar to those of the time domain approach with a strongly reduced computational complexity. In addition, different techniques that can replace the fuzzyc-means clustering algorithm in the derivation of the prototype room response equalizer, with additional reduction of the number of operations, are discussed. Finally, the results of three sets of experiments are used to illustrate the performance, the robustness and the quality of the proposed room response equalization method using alternative prototype design strategies applied to different environments.

In this paper, a bounded-input bounded-output (BIBO) stability condition for the recursive functional link artificial neural network (FLANN) filter, based on trigonometric expansions, is derived. This filter is considered as a member of the class of causal shift-invariant recursive nonlinear filters whose output depends linearly on the filter coefficients. As for all recursive filters, its stability should be granted or, at least, tested. The relevant conclusion we derive from the stability condition is that the recursive FLANN filter is not affected by instabilities whenever the recursive linear part of the filter is stable. This fact is in contrast with the case of recursive polynomial filters where, in general, specific limitations on the input range are required. The recursive FLANN filter is then studied in the framework of a feedforward scheme for nonlinear active noise control. The novelty of our study is due to the simultaneous consideration of a nonlinear secondary path and an acoustical feedback between the loudspeaker and the reference microphone. An output error nonlinearly Filtered-U normalized LMS adaptation algorithm, derived for the elements of the above-mentioned class of nonlinear filters, is then applied to the recursive FLANN filter. Computer simulations show that the recursive FLANN filter, in contrast to other filters, is able to simultaneously deal with the acoustical feedback and the nonlinearity in the secondary path.

The paper discusses computationally efficient NLMS and RLS algorithms for a broad class of nonlinear filters using periodic input sequences. The class comprises all nonlinear filters whose output depends linearly on the filter coefficients. The algorithms presented in the paper are exact, suitable for identification and tracking of every nonlinear system in the class, and require a real-time computational effort of a single multiplication, an addition, and a subtraction per input sample. The transient and steady-state behavior of the algorithms are discussed and the effect of a model mismatch between the unknown system and the adaptive filter is also analyzed. The low computational complexity, good performance, and applicability of the algorithm to a large class of nonlinear systems make the approach of this paper a valuable alternative to the current techniques for nonlinear system identification.

In this paper, a recursive nonlinear filter exploiting trigonometric expansions of the past output samples is introduced. Its peculiarity is, in general, the ability to model real nonlinear systems with a reduced number of coefficients in comparison to finite-memory filters, as IIR filters often do in the linear case. However, the main drawback of recursive models is the need to guarantee, or at least verify, their bounded-input-bounded-output stability. In this paper, a sufficient stability condition is presented, showing that our recursive nonlinear filter is not affected by instabilities when the input signal has finite amplitude and the recursive linear part of the filter is stable. The filter has an interesting application in nonlinear feedforward active noise control since it is able to take into account the acoustic feed back between the loudspeaker, that generates the canceling sound, and the reference microphone, that senses the noise to be canceled, in presence of nonlinear effects. In the paper, a suitable nonlinear adaptation algorithm is derived in such a framework and then applied with good results to a realistic scenario.

In this paper, we propose an extension of the well-known FLANN filter using trigonometric expansions to include suitable cross-terms, i.e., products of input samples with different time shifts. It is shown that in some applications of nonlinear active noise control, the generalized FLANN filter can offer performance as good as that of high-order Volterra filters with a reduced complexity.

The paper discusses computationally efficient NLMS and RLS algorithms for perfect periodic excitation sequences. The most interesting aspect of these algorithms is that they are exact NLMS and RLS algorithms suitable for identification and tracking of every linear system and they require a real-time computational effort of just a multiplication, an addition and a subtraction per sample time. Moreover, the algorithms have convergence and tracking properties that can be comparable to or even better than the NLMS algorithm with a white noise input. The transient and steady state behavior of the algorithms is also studied in the paper.

The paper introduces a self-tuning power scheduling for the auxiliary noise of feedforward active noise control systems with online secondary path modeling. The proposed power scheduling is chosen so that in every operating condition a specific ratio between the powers at the error microphone of the auxiliary noise and of the residual noise is achieved.

The paper introduces two improvements in the feedforward active noise control system with online secondary path modeling developed by Akhtar, Abe, and Kawamata: (1) optimal variable step-size parameters are derived for the adaptation algorithms of the secondary path modeling filter and of the control filter and (2) a self-tuning power scheduling for the auxiliary noise is introduced. The proposed power scheduling is chosen so that in every operating condition a specific ratio between the powers at the error microphone of the auxiliary noise and of the residual noise is achieved. It is shown that for the same auxiliary noise conditions the adaptation algorithms equipped with the optimal variable step-size parameters improve the convergence speed of the system and the estimation accuracy of the secondary path and of the optimal control filter. It is also shown that, compared with a fixed power auxiliary noise, the power scheduling of the auxiliary noise is capable to better meet the conflicting requirements of fast convergence speed of the secondary path modeling filter and of low residual noise in steady state conditions.

The paper provides an analysis of transient and steady-state behavior of a multichannel filtered-x set-membership affine projection algorithm, suitable for multichannel active noise control. The analysis relies on energy conservation arguments and it does not apply the independence theory nor it imposes any restriction to the signal distributions. The analysis results show that the filtered-x set-membership affine projection algorithm can reduce the computational complexity without trading residual mean-square-error and convergence speed

Functional link artificial neural networks exploiting orthogonal functional expansions have been recently proposed as efficient structures for nonlinear active noise control. In this paper it will be shown that simple piecewise-linear expansions can effectively replace trigonometric and orthogonal polynomial expansions. The controller is adapted using an extension of the affine projection algorithm we recently proposed and studied with reference to its transient and steady-state behavior

The paper provides an analysis of the steady-state and transient behavior of two filtered-x affine projection algorithms suitable for multichannel active noise control. The analysis relies on energy conservation arguments, it does not apply the independence theory nor does it impose any restriction to the signal distributions. The paper also shows that filtered-x affine projection algorithms always provide a biased estimate of the minimum-mean-square solution of the active noise control problem.

We consider in this paper a model for nonlinear multichannel active noise controllers based on truncated Volterra filters. A previously proposed approximate affine projection (AP) adaptation algorithm is revisited with the aim of reducing its implementation complexity by applying suitable partial update strategies. The experimental results confirm that remarkable computational reductions are achieved with limited degradations of the convergence behavior.

A multichannel controller based on Volterra filters is described. A filtered-X affine projection algorithm is derived in detail for homogeneous quadratic filters. The proposed algorithm can be also extended to higher-order Volterra kernels and includes linear controllers as a particular case.

This letter presents some theorems for the exact and pth-order equalization of nonlinear systems described by recursive polynomial input-output relationships. It is shown that the nonlinear equalizers derived on the basis of this theory have simple and computationally efficient structures. Furthermore, the pth-order equalizers can be shown to operate in a stable manner for a finite range of values of the input amplitude when the linear component of the nonlinear system being equalized has minimum phase properties.

Many real nonlinear systems are characterized by an infinite input signal memory. In such conditions, system modeling by means of recursive polynomial filters requires a much lower number of coefficients with respect to nonrecursive realizations. However, the main problem of recursive polynomial filters is their inherent instability. This letter describes simple sufficient stability conditions for a class of discrete-time nonlinear systems based on recursive Volterra filters of arbitrary orders.

This paper presents a theory for the exact and the pth order equalization or linearization of nonlinear systems with known recursive or nonrecursive polynomial input-output relationships. The equalizing and linearizing filters have simple and computationally efficient structures. An experimental result that illustrates the good properties of the technique we propose is also included in this paper.

This paper derives sufficient time-varying bounds on the maximum variation of the coefficients of an exponentially stable, linear, time-varying and recursive filter. The stability bound is less conservative than all previously derived bounds for time-varying IIR systems. The bound is then applied to control the step size of output error adaptive IIR filters to achieve exponentially stable operation. Experimental results that demonstrate the good stability characteristics of the resulting algorithms are included in this paper.

We present some theorems for the exact inversion and the pth-order inversion of a wide class of causal, discrete-time, nonlinear systems. The nonlinear systems we consider are described by the input-output relationship y(n)=g[x(n)]h[x(n-1). y(n-1)]+f[x(n-1). y(n-1)], where g[/spl middot/], h[/spl middot/. /spl middot/], and f[/spl middot/. /spl middot/] are causal, discrete-time and nonlinear operators and the inverse function g/sup -1/[/spl middot/] exists. The exact inverse of such systems is given by z(n)=g/sup -1/[{u(n)-f[z(n-1). u(n-1)]}/h[z(n-1). u(n-1)]]. Similarly, when h[/spl middot/. /spl middot/]=1, the pth-order inverse is given by z(n)=g/sub p//sup -1/[u(n)-f[z(n-1), u(n-1)]] where g/sub p//sup -1/[/spl middot/] is the pth-order inverse of g[/spl middot/].