Steven L. Grant

Novel variable step size nlms algorithms for echo cancellation

In this paper we present two new variable step size (VSS) methods for adaptive filters. These VSS methods are so effective, they eliminate the need for a separate double-talk detection algorithm in echo cancellation applications. The key feature of both approaches is the introduction of a new near-end signal energy estimator (NESEE) that provides accurate and computationally efficient estimates even during double-talk and echo path change events. The first VSS algorithm applies the NESEE to the recently proposed Nonparametric VSS NLMS (NPVSS-NLMS) algorithm. The resulting algorithm has excellent convergence characteristics with an intrinsic immunity to double-talk. The second approach is somewhat more ad hoc. It is composed of a combination of an efficient echo path change detector and the NESEE. This VSS method also has excellent convergence, double talk immunity, and computational efficiency. Simulations demonstrate the efficacy of both proposed algorithms.

Proportionate Affine Projection Sign Algorithms for Network Echo Cancellation

Two proportionate affine projection sign algorithms (APSAs) are proposed for network echo cancellation (NEC) applications where the impulse response is often real-valued with sparse coefficients and long filter length. The proposed proportionate-type algorithms can achieve fast convergence and low steady-state misalignment by adopting a proportionate regularization matrix to the APSA. Benefiting from the characteristics of l1-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the proportionate least mean squares (PNLMS) algorithm and the robust proportionate affine projection algorithm (Robust PAPA). The new algorithms also achieve much faster convergence rate in sparse impulse responses than the original APSA and the normalized sign algorithm (NSA). The new algorithms are robust to all types of NEC impulse response with different sparseness without the need to change parameters or estimate the sparseness of the impulse response. The computational complexity of the new algorithms is lower than the affine projection algorithm (APA) family due to the elimination of the matrix inversion.

A Practical Superheterodyne-Receiver Detector Using Stimulated Emissions

The accurate and timely discovery of radio receivers can assist in the detection of radio-controlled explosives. Superheterodyne receivers emit low-power radio signals during normal operation. These are known as unintended emissions. In this paper, the unintended emissions of superheterodyne receivers are analyzed. Such receivers are exposed to known stimulation signals, and their behavior is measured. Recorded emissions demonstrate that it is possible to inject arbitrary signals into a radios unintended emissions using a relatively weak stimulation signal. This effect is called stimulated emissions. A novel detection system that uses these stimulated emissions is proposed. The performance of this system is compared with passive-detection techniques using artificially generated emissions signals. The proposed system offers a 5- to 10-dB sensitivity improvement over existing techniques.

Variable step-size NLMS algorithms designed for echo cancellation

A major issue in echo cancellation is to recover the near-end signal from the error signal of the adaptive filter. In this paper, we use this requirement in order to design a family of variable step-size normalized least-mean-square (VSS-NLMS) algorithms. The main parameter that is needed within these algorithms is the near-end signal power estimate. Several solutions for this problem are presented and evaluated in terms of different practical aspects (i.e., available parameters, complexity). Due to their specific characteristic, these VSS-NLMS algorithms are equipped with good robustness features against near-end signal variations (e.g., double-talk) and can be reliable candidates for real-world echo cancellation scenarios.

Proportionate adaptive filtering for block-sparse system identification

In this paper, a new family of proportionate normalized least mean square (PNLMS) adaptive algorithms that improve the performance of identifying block-sparse systems is proposed. The main proposed algorithm, called block-sparse PNLMS (BS-PNLMS), is based on the optimization of a mixed l2,1 norm of the adaptive filters coefficients. It is demonstrated that both the NLMS and the traditional PNLMS are special cases of BS-PNLMS. Meanwhile, a block-sparse improved PNLMS (BS-IPNLMS) is also derived for both sparse and dispersive impulse responses. Simulation results demonstrate that the proposed BS-PNLMS and BS-IPNLMS algorithms outperformed the NLMS, PNLMS and IPNLMS algorithms with only a modest increase in computational complexity.

An overview on optimized NLMS algorithms for acoustic echo cancellation

Acoustic echo cancellation represents one of the most challenging system identification problems. The most used adaptive filter in this application is the popular normalized least mean square (NLMS) algorithm, which has to address the classical compromise between fast convergence/tracking and low misadjustment. In order to meet these conflicting requirements, the step-size of this algorithm needs to be controlled. Inspired by the pioneering work of Prof. E. Hänsler and his collaborators on this fundamental topic, we present in this paper several solutions to control the adaptation of the NLMS adaptive filter. The developed algorithms are “non-parametric” in nature, i.e., they do not require any additional features to control their behavior. Simulation results indicate the good performance of the proposed solutions and support the practical applicability of these algorithms.

Calibration and 3-D sound reproduction in the Immersive Audio Environment

The effectiveness of virtual environments depends largely on how efficiently they recreate the real world. In the case of auditory virtual environments, the importance of accurate recreation is enhanced since there are no visual cues to assist perception, as in the case of audio-visual virtual environments. In this paper, we present the Immersive Audio Environment (IAE), an easily constructible and portable structure, which is capable of 3-D sound auralization with very high spatial resolution. A novel method for acoustically positioning loudspeakers in space, which is required by the IAE for simulation of sound sources, is presented in this paper. Our contribution is the creation of a system that uses existing and modifications of existing techniques such as Vector Based Amplitude Panning (VBAP) in order to recreate an audio battle environment. The IAE recreates the audio effects of battle scenes and can be used as a virtual battle environment for the training of soldiers. It can also easily be used in other areas such as commercial entertainment.

Variable Regularized Fast Affine Projections

This paper introduces a variable regularization method for the fast affine projection algorithm (VR-FAP). It is inspired by a recently introduced technique for variable regularization of the classical, affine projection algorithm (VR-APA). In both algorithms, the regularization parameter varies as a function of the excitation, measurement noise, and residual error energies. Because of the dependence on the last parameter, VR-APA and VR-FAP demonstrate the desirable property of fast convergence (via a small regularization value) when the convergence is poor and deep convergence/immunity to measurement noise (via a large regularization value) when the convergence is good. While the regularization parameter of APA is explicitly available for on-line modification, FAPs regularization is only set at initialization. To overcome this problem we use noise-injection with the noise-power proportional to the variable regularization parameter. As with their fixed regularization versions, VR-FAP is considerably less complex than VR-APA and simulations verify that they have the very similar convergence properties.

A generalized proportionate adaptive algorithm based on convex optimization

A general framework is proposed to derive proportionate adaptive algorithms for sparse system identification. The proposed algorithmic framework employs the convex optimization and covers many traditional proportionate algorithms. Meanwhile, based on this framework, some novel proportionate algorithms could be derived too. In the simulations, we compare the new derived proportionate algorithm with the traditional ones, and demonstrate that it could provide faster convergence rate and tracking performance for both white and colored input in sparse system identification.

Proportionate affine projection sign algorithms for sparse system identification in impulsive interference

Two proportionate affine projection sign algorithms (APSAs) are proposed for system identification applications, such as network echo cancellation (NEC), where the impulse response is often real-valued with sparse coeficients and long filter length. The proposed proportionate-type algorithms can achieve fast convergence and low steady-state misalignment by adopting a proportionate regularization matrix to the APSA. Benefiting from the characteristic of l1-norm algorithms, affine projection, and proportionate matrix, the new algorithms are robust to impulsive interferences and colored input, and achieve much faster convergence rate in sparse impulse responses than the original APSA, the normalized sign algorithm (NSA), and the proportionate least mean square (PNLMS) algorithm. The computational complexity of the new algorithms is lower than the affine projection algorithm (APA) family due to elimination of matrix inversion.