30 research outputs found
Hybrid feedback active noise control headset based on binaural signal utilization
No full textA standard feedback active noise control (FBANC) headset utilizes the estimate of a primary disturbance at the left-ear (right-ear) error microphone to control noise only at the left (right) ear-cup, i.e., each ear’s controller works independently. In contrast to the FBANC headset, in this paper, a binaural hybrid feedback active noise control (HFBANC) headset is designed that uses the estimate of the primary disturbances at both the left and right-ear error microphones to achieve improved noise control at both the left and right ear-cups. To further improve noise cancellation performance, the nearest Kronecker product decomposition technique is incorporated into the algorithm. The performance of the proposed HFBANC headset is compared to the standard FBANC headset under a variety of different sound field conditions. Experimental results show an improvement of 3–5 dB in the noise cancellation using the proposed algorithms, where the benefits are more prominent for noise sources originating from the side of the user (left and right)
Nonlinear Spline Adaptive Filters based on a Low Rank Approximation
No full textNonlinear spline adaptive filters are a class of adaptive filters for modelling nonlinear systems. To improve the convergence performance of existing nonlinear spline adaptive filters (SAFs), in this paper, we propose a low rank approximation for different SAF models by incorporating the technique of nearest Kronecker product decomposition. We consider the Wiener and Hammerstein SAF models for developing the proposed algorithms, and simulation studies carried out show that improved convergence and tracking performance can be achieved compared to traditional SAFs
Nearest Kronecker Product Decomposition Based Normalized Least Mean Square Algorithm
No full textRecently, 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
Nearest Kronecker Product Decomposition Based Linear-in-The-Parameters Nonlinear Filters
No full textA 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
Fast and efficient acoustic feedback cancellation based on low rank approximation
No full textIn an adaptive feedback cancellation (AFC) scenario, it is essential for an algorithm to track and cancel the feedback signal as quickly as possible. We analyze typical feedback paths in hearing aids and show that they exhibit a low-rank nature. Further, to exploit this knowledge and improve the convergence and tracking performance for AFC, we propose the nearest Kronecker product decomposition based adaptive feedback canceller with prediction error method based signal pre-whitening. Detailed simulation study and comparison of computational complexity show that the proposed algorithm can provide improved convergence and tracking along with improved output speech quality over traditional AFC algorithms, at a moderate computational load
Joint Logarithmic Hyperbolic Cosine Robust Sparse Adaptive Algorithms
No full textRecently, the logarithmic hyperbolic cosine adaptive filter (LHCAF) was proposed and was seen to demonstrate excellent robustness against impulsive interference. However, for the modelling of sparse systems, it may not provide optimal performance as it does not take into account the sparse nature of the system. To improve the modelling accuracy and convergence performance, a sparsity aware zero attraction LHCAF (ZA-LHCAF) and a reweighted zero attraction LHCAF (RZA-LHCAF) is proposed. To further improve the performance for modelling of sparse systems in impulsive environments, a joint logarithmic hyperbolic cosine function (JLHCF) is proposed as the cost function. The corresponding update rule, called the joint logarithmic hyperbolic cosine adaptive filter (JLHCAF) is deduced and the bound on learning rate is derived. A room equalization scenario is also considered and an improved sparsity aware robust algorithm based on JLHCF, namely the filtered-x JLHCAF (Fx-JLHCAF) is proposed for the same. Extensive simulation studies carried out for different system identification scenarios, under Gaussian and non-Gaussian disturbances and a room equalization scenario, demonstrate the superior performance achieved by JLHCAF over existing sparsity aware robust adaptive filters
Low Complexity and Robust Diffusion Affine Projection Algorithms for Distributed Estimation
No full textDiffusion affine projection algorithms have the ability to de-correlate the input signal and have faster convergence but with the expense of increased computational complexity. Moreover, traditional diffusion affine projection algorithms consider the noise to be of Gaussian nature. However, practically this noise can be non-Gaussian which can significantly deteriorate the convergence of the algorithms. To mitigate this issue in this brief, we propose two robust affine projection algorithms based on the generalized maximum correntropy criterion (d-A-GMCC) and the logarithmic hyperbolic cosine cost function (d-A-lncosh). To reduce the computational expense of the proposed algorithms, we propose dichotomous coordinate descent based d-A-GMCC and d-A-lncosh algorithms. Extensive simulation study for different Gaussian and non-Gaussian noise environments shows the improved estimation ability of proposed algorithms
Nearest Kronecker product decomposition based generalized maximum correntropy and generalized hyperbolic secant robust adaptive filters
No full textRobust 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
Generalized Soft-Root-Sign Based Robust Sparsity-Aware Adaptive Filters
Get PDFRobust 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
Study of Sparsity Emanating from NKPD and its Utilization to Enhance NKPD based Adaptive Algorithms
Get PDFRecently, the nearest Kronecker product (NKP) decomposition has become popular in several adaptive filtering (AF) applications owing to its fast convergence and tracking ability. In this paper, we study the nature of the smaller weight vectors resulting from NKP decomposition (NKPD) of a wide range of acoustic impulse responses (IRs). The study shows that the smaller weight vectors resulting from NKPD exhibit moderate to high degree of sparsity. To exploit this knowledge in AF problems, we propose a class of proportionate update based NKP normalized least-mean-square (NKP-NLMS) type algorithms: namely, the improved proportionate NKP-NLMS (NKP-IPNLMS) algorithm which uses the ℓ1-norm of the smaller weight vectors and the NKP-IPNLMS-ℓ0 which uses an approximation of the ℓ0-norm. Further, we propose a new approximation of the ℓ0-norm with reduced computational complexity, using which we also propose the NKP-IPNLMS-ℓ0-2 algorithm. Next, we present a comparison of computational complexity of the proposed algorithms. Simulation results show the improved performance achieved by the proposed algorithms, showing the advantage of exploiting sparsity in the smaller weight vectors in NKPD based adaptive algorithms
