Performance of sound zone control (SZC) systems deployed in practical scenarios are highly sensitive to the location of the listener(s) and can degrade significantly when listener(s) are moving. This paper presents a robust SZC system that adapts to dynamic changes such as moving listeners and varying zone locations using a dictionary-based approach. The proposed system continuously monitors the environment and updates the fixed control filters by tracking the listener position using audio signals only. To test the effectiveness of the proposed SZC method, simulation studies are carried out using practically measured impulse responses. These studies show that SZC, when incorporated with the proposed audio-only position tracking scheme, achieves optimal performance when all listener positions are available in the dictionary. Moreover, even when not all listener positions are included in the dictionary, the method still provides good performance improvement compared to a traditional fixed filter SZC scheme.

Sound zone control (SZC) implemented using static optimal filters is significantly affected by various perturbations in the acoustic environment, an important one being the fluctuation in the speed of sound, which is in turn influenced by changes in temperature and humidity (TH). This issue arises because control algorithms typically use pre-recorded, static impulse responses (IRs) to design the optimal control filters. The IRs, however, may change with time due to TH changes, which renders the derived control filters to become non-optimal. To address this challenge, we propose a straightforward model called sinc interpolation-compression/expansion-resampling (SICER), which adjusts the IRs to account for both sound speed reduction and increase. Using the proposed technique, IRs measured at a certain TH can be corrected for any TH change and control filters can be re-derived without the need of re-measuring the new IRs (which is impractical when SZC is deployed). We integrate the proposed SICER IR correction method with the recently introduced variable span trade-off (VAST) framework for SZC, and propose a SICER-corrected VAST method that is resilient to sound speed variations. Simulation studies show that the proposed SICER-corrected VAST approach significantly improves acoustic contrast and reduces signal distortion in the presence of sound speed changes.

Existing literature on sound zone control generally consider the signal model to be linear. However, this is seldom true in practice owing to nonlinear distortions arising from the loudspeakers, especially in consumer applications. In this paper, we propose a new signal model for personal sound zone control that takes into consideration any nonlinear behaviour that may arise from the loudspeakers. Following the proposed signal model, the optimization problem is formulated such that it inherently ensures the reduction of nonlinear distortion effects in both the bright and dark zones. In addition, a broadband nonlinear acoustic contrast control - pressure matching approach is proposed for the new signal model. Simulation results on practical data show that our proposed approach can provide improvement in the acoustic contrast and/or the signal distortion performance compared to the traditional linear solution, in the presence of nonlinear distortions. Moreover, important observations are made for the study of nonlinear effects on sound zone control.

The performance of sound zone control algorithms are known to degrade significantly with changes in acoustic conditions including perturbations of control microphones' positions. In this work, we study the feasibility and effectiveness of using low rank approximations of RIRs to calculate sound zone control filters, to improve the robustness of sound zone control algorithms to perturbations in the bright zone (BZ) microphones. For algorithm design, we consider the framework of variable span linear filter (VSLF) which allows a wide range of user selectivity between acoustic contrast (AC) and signal distortion (SD) trade off, including acoustic contrast control (ACC) and pressure matching (PM) methods as special cases. Detailed simulation study shows that above a certain rank of the variable span trade-off (VAST) filter, the proposed approach using low rank RIRs to derive the control filters provides higher AC compared to using full rank RIRs, when there are perturbations in BZ microphone positions.

Robust adaptive filters utilizing hyperbolic cosine and correntropy functions have been successfully employed in non-Gaussian noisy environments. However, these filters suffer from high steady-state misalignment due to significant weight update in the presences of outliers. In addition, several practical systems exhibit sparse characteristics, which is not taken into account by these filters. In this paper, a generalized soft-root-sign (GSRS) function is proposed and the corresponding GSRS adaptive filter is designed. The proposed GSRS provides negligible weight update in the occurrence of large outliers and thereby results in lower steady-state misalignment. To further improve modelling performance for sparse systems and to achieve robustness, sparsity-aware GSRS algorithms are also developed in this paper. The bound on learning rate and the computational complexity of proposed algorithm is also investigated. Simulation studies confirmed the improved convergence characteristics achieved by the proposed algorithms over existing algorithms.

In recent years, correntropy-based algorithms which include maximum correntropy criterion (MCC), generalized MCC (GMCC), kernel MCC (KMCC) and hyperbolic cosine function-based algorithms such as hyperbolic cosine adaptive filter (HCAF), logarithmic HCAF (LHCAF), least lncosh (Llncosh) have been widely utilized in adaptive filtering due to their robustness towards non-Gaussian/impulsive background noises. However, the performance of such algorithms suffers from high steady-state misalignment. To minimize the steady-state misalignment along with having comparable computational complexity, an exponential hyperbolic cosine function (EHCF) based new robust norm is introduced and a corresponding EHCF based adaptive filter called exponential hyperbolic cosine adaptive filter (EHCAF) is developed in this letter. Further, computational complexity and bound on learning rate for stability of the proposed algorithm is also studied. A set of simulation studies has been carried out for system identification scenario to assess the performance of the proposed algorithm. Further, EHCAF algorithm has been extended and the filtered-x EHCAF (Fx-EHCAF) algorithm is proposed for robust room equalization.

A linear-in-the-parameters nonlinear filter consists of a functional expansion block, which expands the input signal to a higher dimensional space nonlinearly, followed by an adaptive weight network. The number of weights to be updated depends on the type and order of the functional expansion used. When applied to a nonlinear system identification task, as the degree of the nonlinearity of the system is usually not known a priori, linear-in-the-parameters nonlinear filters are required to update a large number of coefficients to effectively model the nonlinear system. However, all the weights of the nonlinear filter may not contribute significantly to the identified model. We show via simulation experiments that, the weight vector of a linear-in-the-parameters nonlinear filter usually exhibits a low-rank nature. To take advantage of this observation, this paper proposes a class of linear-in-the-parameters nonlinear filters based on the nearest Kronecker product decomposition. The performance of the proposed filters is superior in terms of convergence behaviour as well as tracking ability in comparison to their traditional linear-in-the-parameters nonlinear filter counterparts, when tested for nonlinear system identification. Furthermore, the proposed nearest Kronecker product decomposition-based linear-in-the-parameters nonlinear filters has been shown to provide improved noise mitigation capabilities in a nonlinear active noise control scenario.

The adaptive exponential functional link network (AEFLN) is a recently introduced novel linear-in-the-parameters nonlinear filter and is used in numerous nonlinear applications, including system identification, active noise control, and echo cancellation. The improved modeling accuracy offered by AEFLN for different nonlinear applications can be attributed to the exponentially varying sinusoidal basis functions used for nonlinear expansion. Even though AEFLN has been widely used for the identification of nonlinear systems, no theoretical analysis of AEFLN is available in the literature. Hence, in this article, a theoretical performance analysis of AEFLN trained using an adaptive exponential least mean square (AELMS) algorithm under the Gaussian input assumption is discussed. Expressions describing the mean as well as mean square behavior of the weight vector and adaptive exponential parameter are derived. Computer simulations are carried out, and the derived theoretical expressions show a close correspondence with simulation results.

Recently, nearest Kronecker product (NKP) decomposition based Wiener filter and Recursive Least Squares (RLS) have been proposed and was found to be a good candidate for system identification and echo cancellation and was shown to offer better tracking performance along with lower computational complexity, especially for identification of low-rank systems. In this paper, we derive the Least Mean Square (LMS) versions of adaptive algorithms which take advantage of NKP decomposition, namely NKP-LMS and NKP Normalized LMS (NKP-NLMS) algorithms. We compare the convergence and tracking performance along with computational complexity between standard NLMS, standard RLS, NKP based RLS (RLS-NKP), the standard Affine Projection Algorithm (APA) and NKP-NLMS algorithm, to evaluate the efficacy of NKP-NLMS algorithm in the context of system identification. Simulation results show that NKP-NLMS can be a good candidate for system identification, especially for sparse/low rank systems.

Robust adaptive signal processing algorithms based on a generalized maximum correntropy criterion (GMCC) suffers from high steady state misalignment. In an endeavour to achieve lower steady state misalignment, in this letter we propose a generalized hyperbolic secant function (GHSF) as a robust norm and derive the generalized hyperbolic secant adaptive filter (GHSAF). The new algorithm is seen to offer robust system identification performance over the conventional GMCC algorithm. To further improve the convergence performance under non-Gaussian noise environments, we propose the nearest Kronecker product decomposition based GMCC and GHSAF algorithms. Extensive simulation study show the improved convergence performance provided by the proposed algorithms for system identification.