A new family of block-sparse proportionate affine projection algorithms (BS-PAPA) is proposed to improve the performance for block-sparse systems. This is motivated by the recent block-sparse proportionate normalized least mean square (BS-PNLMS) algorithm. It is demonstrated that the affine projection algorithm (APA), proportionate APA, (PAPA), BS-PNLMS and PNLMS are all special cases of, the proposed BS-PAPA algorithm. Meanwhile, an efficient implementation of the proposed BS-PAPA and block-sparse memory PAPA (BS-MPAPA) are also presented to reduce computational complexity. Simulation results demonstrate that the proposed BS-PAPA and BS-MPAPA algorithms outperform the APA, PAPA and MPAPA algorithms for block-sparse system identification in terms of both faster convergence speed and better tracking ability.

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 filter's 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.

This paper proposes an improved variable step-size (VSS) scheme for zero-point attracting projection (ZAP) algorithm. The proposed VSS is proportional to the sparseness difference between filter coefficients and the true impulse response. Meanwhile, it works for both sparse and non-sparse system identification, and simulation results demonstrate that the proposed algorithm could provide both faster convergence rate and better tracking ability than previous ones.

Acoustic echo cancellation (AEC) is a well studied problem. The underlying assumption in most echo cancellation solutions is that the echo path following the reference signal is completely linear. However, in many handheld devices, the echo path following the reference signal is nonlinear. The reason for this nonlinearity in the echo path is the use of smaller inexpensive loudspeakers and the desire for generating high sound pressure levels. This brings about the need for a nonlinear echo canceler to maintain the required echo return loss enhancement (ERLE). Many software-based solutions have been proposed to solve this problem, but the computational complexity of these solutions is prohibitively high for practical implementation. This paper analyzes the sources of nonlinearities in smartphones and proposes a simple, elegant hardware modification to significantly reduce nonlinear echo. Thorough analysis and intensive testing results show that up to 6 dB of improvement in ERLE in a real device is possible using the proposed technique.

A sub-threshold design could provide a compelling approach to power critical applications. An exponential relationship exists, however, between the delay and the threshold voltage, that makes this design-time timing closure extremely difficult, if not impossible, to achieve. Several previous studies were focused on the technique of body biasing during post-silicon tuning for delay compensation. But they were mostly for super-threshold designs where spatially correlated ${L} _{\mathbf {eff}}$ variation dominates. They cannot be applied directly to sub-threshold designs in which purely random threshold voltage variations dominate. These works also assumed multiple body biasing voltage domains and multiple body biasing voltage levels, which involve significant design overhead. The problem of selective body biasing for post-silicon tuning of sub-threshold designs is examined in this paper. The possibility of using only one body bias voltage domain with a single body bias voltage is explored. The problem was formulated first as a linearly constrained statistical optimization model. The adaptive filtering concept from the signal processing community was then adopted so that an efficient, yet novel, solution could be developed. Using several 65 nm industrial designs, experimental results suggest that, compared with a seemingly more intuitive approach, the proposed approach can improve the pass rate by 57% on average with similar standby power and the same number of body biasing gates. This approach can reduce the standby power, on average, by 84%, with a 20% pass rate loss, more than the approach to bias all the gates.

In echo cancellation, the main goal is to recover the near-end signal from the error signal of the adaptive filter, which identifies the echo path. In this context, the Kalman filter represents a very appealing choice, since its basic criterion follows the minimization of the system misalignment (instead of the usual error-based cost function). In this paper, we propose a Kalman filter with individual control factors, in terms of using a different level of uncertainty for each coefficient of the filter. As compared to the basic Kalman filter (which imposes the same uncertainty for all the coefficients of the impulse response), the proposed algorithm achieves better performance, especially in terms of the steady-state misalignment.

This paper introduces two new frequency domain overdetermined blind source separation (BSS) algorithms: Inter-frequency Correlation with Microphone Diversity (ICMD), and ICA with Triggered Principal component analysis (ITP). In the first, we consider different sets of microphones, where in each set the number of microphones and sources are equal. In the second, we extract principal components from an overdetermined mixture to form a determined mixture for separation. Both techniques utilize inter-frequency correlation to align permutations via energy profiles. Both monitor the condition number of an inter-frequency cross-correlation matrix of the normalized de-mixed signals’ envelopes to determine if separation has failed for the current ICA input configuration; if so, the input configuration is revised and efficiently realigned to produce a better mixture for separation. The complexities and performances of these algorithms are examined in both simulations and a real-room measurement, with three and five sources. They are also compared to other recent frequency domain BSS algorithms for benchmarking purposes. Results show that generally, ICMD and ITP show similar performance with each other and with one of the benchmarking algorithms. However, ICMD is more computationally efficient.

In a previous study, the performance of an acoustic feedback/echo cancellation system was analyzed using a power transfer function method. Whereas the analysis result provides very accurate performance predictions in open-loop acoustic echo cancellation systems, it is less accurate in closed-loop acoustic feedback cancellation systems if there is a strong correlation between the loudspeaker signal and the signals entering the microphones. This work extends the performance analysis to include the effects of the nonzero correlation on the adaptive filters. Simulation results verify that this extension provides much more accurate performance predictions in closed-loop acoustic feedback cancellation systems.

Acoustic echo cancellation (AEC) is a well studied problem. One of the main assumptions in existing AEC techniques is that the echo path is linear. In today's equipment, this assumption does always hold. The increasing focus on the look and feel of commercial devices and consequently the decreasing size of their loudspeakers are the major contributors to making the echo path nonlinear. This emphasizes the need for a nonlinear echo canceler to maintain the required echo return loss enhancement (ERLE). Many algorithms have been proposed to solve this problem, but they are prohibitive because of their high computational complexity. This paper proposes a hardware modification to significantly reduce the nonlinear echo. Results show that up to 6 dB of improvement in ERLE in a real device are possible.

This paper presents a new algorithm for addressing the permutation ambiguity in convolutive blind source separation. The proposed algorithm seeks to prevent permutations by frequency oversampling, and then exploiting the induced correlation between bins. Any remaining permutation is then corrected by beam pattern estimation. Cascade initialization is shown to improve system performance while decreasing processing time, while frequency oversampling is shown to increase performance with slight increases in computational time. The algorithm is proven robust against isotropic noise, producing a SIR improvement of 12.9 dB when the reverberation time is 260 ms, and the signals at the input are only 5 dB above the noise floor.

Basis pursuit has been shown to be an effective method of solving inverse problems with a small amount of data when the system to be determined has a sparse representation. Adaptive filters fall under this general category of problems. Here, we use the echo cancellation context to introduce a method of solving the basis pursuit problem with an iterative method based on the proportionate normalized affine projection algorithm (PAPA). Earlier, it has been shown that PAPA can be derived from a basis pursuit perspective. Here we refine the assumptions made in those derivations and show that an iterative form of PAPA yields the same results as basis pursuit without resorting to the simplex method. The resulting algorithm has extremely fast convergence for adaptive filters with very sparse impulse responses. Simulations using the new iterative approach are also presented.

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 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 characteristic of Zi-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.

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.

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.

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.

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, FAP's 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.

In this paper, we present two different double-talk detection schemes for Acoustic Echo Cancellation (AEC). First, we present a novel normalized detection statistic based on the cross-correlation coefficient between the microphone signal and the cancellation error. The decision statistic is designed in such a way that it meets the needs of an optimal double-talk detector. We also show that the proposed detection statistic converges to the recently proposed normalized cross-correlation based double-talk detector, the best known cross-correlation based detector. Next, we present a new hybrid double-talk detection scheme based on a cross-correlation coefficient and two signal detectors. The hybrid algorithm not only detects double-talk but also detects and tracks any echo-path variations efficiently. We compare our results with other cross-correlation based double-talk detectors to show their effectiveness.

A robust adaptive beamformer is proposed using the near field regionally constrained adaptive approach that designs a set of linear constraints by filtering on a low rank subspace of the near field signal over a spatial region and a wide frequency band. This method can accurately control the beamformer response over the designed spatial-temporal region using a small number of linear constraint vectors and improve the robustness against target location errors. Meanwhile, this method enhances the capability of the near field beamformer in distance discrimination without additional constraints so that interference impinging at the same direction as the desired signal but at a different distance can be effectively suppressed

This paper introduces a new type of adaptive filter called PCP, principal component proportionate normalized least mean squares. It is an extension of PNLMS (proportionate normalized least mean squares), an adaptive filter that has been shown to provide exceptionally fast convergence and tracking when the underlying system parameters are sparse (as in network echo cancellation). PCP extends the application of PNLMS to certain nonsparse systems by applying it while using the principal components of the underlying system as basis vectors. Room acoustic echo systems are possible examples of such nonsparse systems. Simulations of acoustic echo paths and cancellers indicate that PCP converges and tracks much faster than the classical normalized least mean squares (NLMS) and fast recursive least squares (FRLS) adaptive filters.