In this paper, we present a cross evaluation of different a priori SNR estimation methods as well as different time-frequency analysis processing using a subjective listening test. The noisy signal is corrupted by different types of background noise i.e. babble and pink, and varying levels of input SNR (0 dB and 10 dB). The signals are processed using Short Time Fourier Transform (STFT) or Critical Band (CB) processing. After estimating the clean speech signal, it was presented to 10 participants for evaluation using a subjective listening test according to (ITU-TP.835) methodology. The results demonstrate that the participants preferred the speech signal processed using CB for low SNR levels and non-stationary background noise, which means that critical band based frequency scale is more useful in adverse noisy conditions.

A challenge in hearing aids is adaptive feedback control which often uses an adaptive filter to estimate the feedback path. This estimate of the feedback path usually results in a bias due to the correlation between the loudspeaker signal and the incoming signal. The prediction error method (PEM) is a popular method for reducing this bias for adaptive feedback control (AFC) in hearing aids, providing a significant performance improvement compared to conventional adaptive feedback control techniques. However, the PEM-based AFC (PEM-AFC) applications are still limited to single-microphone single-loudspeaker (SMSL) systems. This paper investigates the application of the PEM-AFC to a two-microphone single-loudspeaker hearing aid with detailed theoretical analysis as well as practical experiments. In the proposed method, PEM-AFC2, we use the two-microphone adaptive feedback control (AFC2) method with two microphones and one loudspeaker. The incoming signals at the two microphones are related by a relative transfer function (RTF) which is used to predict the incoming signal at the main microphone. In addition, a prefilter is employed to prewhiten the loudspeaker and the microphone signals before the adaptive filter estimates. As a result, the proposed method obtains a lower bias and a faster tracking rate compared to the PEM-AFC and the AFC2 method, while still maintaining a good quality of the incoming signal. A new derivation for optimal filters in the AFC2 method will also be provided. The performance of the proposed method is evaluated for speech shaped noise as incoming signal and with undermodeling the RTF as well as with perfect modeling the RTF. Moreover, different types of incoming signals and a sudden change of feedback paths are also considered. The experimental results show that the proposed approach yields a significant performance improvement compared to existing state-of-the-art AFC methods such as the PEM-AFC and the AFC2.

In this paper, a modified a priori SNR estimation method with application in speech enhancement is presented. The decision directed (DD) approach for the a priori SNR estimation has been the most popular method due to its ability to eliminate musical noise in speech enhancement. However, a limitation of this method is the slow tracking of speech onsets that generates a transient distortion to the speech. The proposed method modifies the DD a priori SNR estimator by employing an adaptive smoothing factor based on the instan-taneous a posteriori SNR and an adaptive sigmoid function to obtain an improved tracking of speech onsets. Experimental results using several instrumental measures show the ability of the proposed method to preserve weak speech components when compared to two reference methods while maintaining the advantage of the DD approach in eliminating the musical noise.

The paper proposes a convex combination fusion function based on a sigmoid function for the estimation of the a priori SNR in a speech enhancement framework with critical frequency band processing. The proposed method does not only eliminate the one frame delay generated by the well-known decision directed approach but also increases the adaptation speed during abrupt changes in the SNR estimation. As a result, the advantage of low musical noise has been maintained while more weak speech components have been preserved. Experimental results using instrumental and subjective measures also indicate improvement in speech quality compared to the reference methods.

The proportionate normalized least-mean-squares (PNLMS) algorithm is commonly used in acoustic echo cancellation (AEC) context. It provides faster initial convergence and tracking rates compared to the NLMS algorithm for the case of sparse echo impulse responses. The improved PNLMS algorithm (IPNLMS) has been proven to be more powerful than PNLMS by exploiting new rules for computing the weight of each step-size corresponding to each adaptive filter coefficient. However, the application of the PNLMS and the IPNLMS algorithms for adaptive feedback control (AFC) in hearing aids (HAs) is still limited due to high correlation between the loudspeaker and incoming signals. This paper proposes implementations of the PNLMS/IPNLMS algorithms for AFC using the prediction error method (PEM) for hearing aids. The proposed methods have been evaluated for both speech and music incoming signals. Simulation shows that the proposed methods have faster initial convergence and tracking than the PEM using the NLMS algorithm (PEM-NLMS).

This letter proposes an optimal design of the filter bank with minimum inband aliasing, residual aliasing together with restrictions on the frequency response of the analysis filter and the distortion level for the filter bank. A new procedure that includes the combination of one parameter filled function and an iterative method is developed to solve the optimization problem. The advantage of using the iterative algorithm within the filled function is that a lower basin can be obtained with a low computational complexity. Design examples show that a significant improvement can be obtained using the proposed method over existing methods.

Zero-forcing beamforming (ZFBF) is a popular pre-coding scheme for MIMO systems. Most of the studies in the literature are under total power constraints. However, the per-antenna power constraints (PAPC) are more realistic. The state-of-the-art method is interior point method which is expensive to realize in practice due to the high computational complexity. Hence, a low complexity zero-forcing precoding scheme under the per-antenna power constraints is proposed in this paper. This is achieved by introducing a regularized dual method. Simulations are carried out to show the effectiveness of the proposed method, which has a low computational complexity. In addition, the algorithm can be implemented in parallel to further reduce the computational complexity.

In industrial noise environments, the use of assistive listening headsets is a means to provide adequate access to voice communication while wearing hearing protection. This paper presents a performance evaluation and comparison of two different methods to provide the binaural speech enhancement in real industrial noise scenarios. The investigated binaural methods based on differential beamforming and multichannel Wiener filter show different strengths and weaknesses. A transient noise suppression algorithm is also proposed and evaluated. Performance evaluation shows that this algorithm, together with the binaural multi-channel Wiener filter approach, can successfully reduce the hammering noise. This can be observed from the PESQ scores and the signal characteristics.

In this letter, we develop an algorithm for the design of allpass variable fractional delay filter with powers-of-two coefficients as a means of achieving low complexity and efficient hardware implementation. The algorithm includes an efficient procedure to obtain the objective function in the neighborhood of a search point, which significantly reduces the complexity associated with the global optimization approach. Design examples show that significant improvement is obtained by using the proposed method over existing methods.

This letter proposes a new algorithm for solving the optimal zero-forcing beamforming problem that maximizes the user achievable rate with restriction on the per-antenna element power constraints. An accelerated gradient method with step size search is proposed for solving the problem. For each iteration of the gradient approach, a quick one dimensional search is employed to obtain the step size. The advantage of the step-size search is that it is relatively fast and requires only a few calculations of the objective function. Design examples show that the proposed algorithm converges faster than the gradient approach and the accelerated gradient approach with a constant step size while achieving a low computational complexity.

This letter develops an efficient computational procedure for the design of an odd-order variable fractional delay (VFD) digital filter with minimum peak variable fractional delay error subject to restrictions on the peak variable frequency response error. An iterative algorithm is developed to solve the formulated non-linear optimization problem where a first order linear approximation is employed for the variable fractional delay error. This results in a second-order cone programming (SOCP) problem with linear constraints. Design examples show that the peak fractional delay error can be reduced significantly from the minimax solution while maintaining approximately the same peak frequency response error. In addition, for a fixed level of peak variable frequency response error, approximately 1.61-2.09 dB improvement for the peak variable delay error can be obtained over existing methods.

Binaural noise-reduction algorithms based on multi-channel Wiener filter (MWF) are promising techniques to be used in binaural assistive listening devices. The real-time implementation of the existing binaural MWF methods, however, involves challenges to increase the amount of noise reduction without imposing speech distortion, and at the same time preserving the binaural cues of both speech and noise components. Although significant efforts have been made in the literature, most developed methods so far have focused only on either the former or latter problem. This paper proposes an alternative binaural MWF algorithm that incorporates the non-stationarity of the signal components into the framework. The main objective is to design an algorithm that would be able to select the sources that are present in the environment. To achieve this, a modified speech presence probability (SPP) and a single-channel speech enhancement algorithm are utilized in the formulation. The resulting optimal filter also avoids the poor estimation of the second-order clean speech statistics, which is normally done by simple subtraction. Theoretical analysis and performance evaluation using realistic recorded data shows the advantage of the proposed method over the reference MWF solution in terms of the binaural cues preservation, as well as the noise reduction and speech distortion.

The real-time implementation of the existing multi-channel Wiener filter (MWF) algorithms suffer from performance degradation due to the lack of robustness against estimation errors of the second-order statistics. The reasons are twofold: one, the estimation of the statistics relies on real voice activity detector (VAD), which often fails in adverse environments. Second, the MWF solutions involve estimation of the second order clean speech statistics, which also exaggerates the errors. This paper presents an MWF algorithm that requires neither VAD nor clean speech statistics. Performance evaluation under real scenarios shows that the proposed method outperforms the conventional MWF solution in terms of the trade-off between noise reduction and speech distortion.

In this paper, a modified a priori SNR estimator is proposed for speech enhancement. The well-known decision-directed (DD) approach is modified by matching each gain function with the noisy speech spectrum at current frame rather than the previous one. The proposed algorithm eliminates the speech transient distortion and reduces the impact from the choice of the gain function towards the level of smoothing in the SNR estimate. An objective evaluation metric is employed to measure the trade-off between musical noise, noise reduction and speech distortion. Performance is evaluated and compared between a modified sigmoid gain function, the state-of-the-art log-spectral amplitude estimator and the Wiener filter. Simulation results show that the modified DD approach performs better in terms of the trade-off evaluation.

We propose a numerically efficient technique to approximate the joint detection probability of a coherent multiple input multiple output (MIMO) receiver in the presence of inter-symbol interference (ISI) and additive white Gaussian noise (AWGN). This technique approximates the probability of detection by numerically integrating the product of the characteristic function (CF) of the received filtered signal with the Fourier transform of the multi-dimension decision region. Naturally, the accuracy of the approximation is dependent on the number of points in the numerical integration. The proposed method selects the number of points to integrate over by deriving bounds on the approximation error. As such, a reduction of up to 1/100thof the number points can be achieved when comparing with existing single input single output (SISO) techniques. Simulation results demonstrate that the proposed method is highly accurate with the calculated errors being well within the bounded approximation error. In addition, the results are closely matched with those obtained using the Monte Carlo simulations with a typical error less than 10-4while requiring only a fraction of the computation time.

The number of spreading sequences required for direct-sequence code-division multiple-access (DS-CDMA) systems depends on the number of simultaneous users in the system. Often a sequence family provides more sequences than are required; in many cases the selection of the employed sequences is a computationally intensive task. This selection is a key consideration, as the properties of the sequences assigned affect the error performance in the system. In this paper, a branch and bound algorithm is presented to perform this selection based on two different cost functions. Numerical results are presented to demonstrate the improved performance of this algorithm over previous work.

This letter presents a new computational procedure for the second-order gradient-based blind signal separation (BSS) problem with convolutive mixtures that has improved convergence characteristics over the steepest descent algorithm. The BSS problem is formulated as a constrained optimization problem with complex unmixing weight matrices where the constraints are formulated to overcome the permutation effects. This problem is then transformed into an unconstrained optimization problem, so that the conjugate gradient algorithm can be applied. The convergence of the proposed procedure is compared with the steepest descent algorithms in real and simulated environments.

This paper addresses the problem of extracting a desired speech source from a multispeaker environment in the presence of background noise. A new adaptive beamforming structure is proposed for this speech enhancement problem. This structure incorporates power spectral density (PSD) estimation of the speech sources together with a noise statistics update. An inactive-source detector based on minimum statistics is developed to detect the speech presence and to track the noise statistics. Performance of the proposed beamformer is investigated and compared to the minimum variance distortionless response (MVDR) beamformer with or without a postfilter in a real hands-free communication environment. Evaluations show that the proposed beamformer offers good interference and noise suppression levels while maintaining low distortion of the desired source.

Large area (LA) sequences form a class of ternary spreading sequences which exhibit an interference free window. In addition, these sequences have low correlation properties outside this window. Work to date has concentrated on examining the parameters and performance of LA sequences with reference to only a small number of example families. In this paper we develop general conditions which an LA family must satisfy. The development of these conditions allows for the production of computationally efficient tests to determine whether a given family is an LA family. In particular, these tests can form the basis for algorithms to construct LA families, allowing for a larger number of families with the potential for higher energy efficiency than those of previous work.

Numerous spread spectrum applications require families of long spreading sequences, often used at very high chip rates. In this paper, an algebraically simple way of generating long sequences by combining shorter polyphase sequences is presented, aimed at asynchronous spread spectrum systems. The approach is motivated by the fact that polyphase sequences offer increased design options, in terms of the supported range of correlation characteristics, while combination sequences allow for simpler generation of long sequences. It leads to the definition of combination Oppermann sequences, the properties of which are investigated in this paper. Numerical results indicate that the families of these proposed combination sequences provide favourable aperiodic correlations. The presented family of combination Oppermann sequences is therefore suitable for applications that rely on rapid synchronisation and are required to provide multiple access to the system.

In this paper we introduce periodic Oppermann sequences, which constitute a special class of polyphase sequences. The properties of these sequences are presented, and indicate that periodic Oppermann sequences are suitable for combination to generate families of longer sequences. Numerical examples show that periodic Oppermann sequences can be designed for ranging or synchronisation applications or for supporting multiple access spread spectrum communication systems.

This paper presents a new normalized frequency domain approach for adaptive blind equalization for multiple-input multiple-output (MIMO) communication systems. We first develop a time domain block based algorithm that updates the equalizer coefficients once per block of data symbols. As the time domain algorithm involves infinite summations in the separation cost function, an approximation is then proposed to reduce the cost function to finite summations. As a consequence, the block based time domain algorithm can be implemented in the frequency domain to reduce the computational complexity associated with the conventional symbol-by-symbol time domain update. Furthermore, by recognizing that the signals in the frequency domain are orthogonal, it is possible to significantly improve the convergence rate by normalizing the update equations with respect to the signal power in each frequency bin. Simulation results show that the proposed algorithm can successfully equalize the received signals while maintaining low computational complexity. Moreover, the presented normalized frequency domain blind equalization algorithm for MIMO systems significantly improves the convergence rate.

Finite-length multiple-input multiple-output (MIMO) decision feedback equalizers (DFEs) that optimize the decision delays are investigated herein. The mean square error (MSE) of each output with respect to the decision delays is derived to form the basis of the optimization cost function. A genetic algorithm (GA) is implemented to reduce the computational burden of the search. Simulation results show that the GA successfully tracks the optimum decision delays with only a fraction of the computational complexity of the global search. Moreover, the use of optimized decision delays in the MIMO DFE for simulated fading MIMO channels provides an average improvement of 4.1 dB for the combined output MSE.

The multi-rate subband equaliser converts the computationally complex fullband equaliser structure into several parallel low complexity subband equalisers, each operating independently. The fullband equaliser has a minimum mean square error (MMSE) dependent upon the filter coefficients and the relative delay between the desired symbol and received signal. The subband equalisers MMSE performance is also dependent upon the subband adaptive filter (SAF) coefficients and the relative delay between the desired and received subband signal components, referred herein as the alignment delay. We propose a new structure allowing implementation of independent alignment delays for each subband to achieve the MMSE with respect to both the SAF coefficients and the alignment delay. Simulation results show that the use of independent alignment delays in each subband provides significant improvement over the standard subband transversal equaliser.

This paper presents a new space constrained adaptive beamformer employing an updated source power spectral density (PSD). The space constraints are used to capture the target signal spatially and to provide robustness against steering error vectors. The PSD update on the other hand ensures that the spectral information of the desired source is reflected continuously on the space constraints. As such, target signal extraction can be achieved with minimum distortion. The beamformer operates in a subband structure to allow time-frequency operation for each channel, yielding a combination of weighted spatial and temporal filters. Evaluations on real car data show that the proposed algorithm significantly improves the speech intelligibility with noise suppression level up to 21 dB.

Broadband wireless systems require powerful equalizers which can cope with long channel impulse responses and severe intersymbol interference (ISI). In this paper, a frequency domain modified constant modulus algorithm (PD-MCMA) is presented. The employed modified CMA (MCMA) is chosen to confine the phase ambiguity of the conventional CMA to a 90/spl deg/ phase shift solution. On this foundation, a block MCMA algorithm which updates the equalizer coefficients on a block-by-block basis is proposed. The advantage of this block MCMA is that it can be equivalently implemented in the frequency domain. This approach offers a large reduction in computational complexity compared to time domain algorithms. For an equalizer with 256 coefficients, the number of real multiplications required by the FD-MCMA is only 9.5% of that required by the MCMA. Furthermore, convergence of the proposed FD-MCMA is significantly improved by properly normalizing the update equations. Simulations show that the normalized FD-MCMA converges much faster than the MCMA and the FD-MCMA while maintaining approximately the same bit error rate (BER) performance.

The performance of a code division multiple access system depends on the correlation properties of the employed spreading code. An auto-correlation function with a distinct peak enables proper synchronization and suppresses intersymbol interference. Low cross-correlation values between spreading sequences are desired to suppress multiple access interference. However, these requirements contradict each other and a trade-off needs to be established. In this paper, a modified bridging method is proposed to minimize cross-correlation with auto-correlation being allowed to lie within a fixed region. This approach is applied to the design of complex spreading sequences.

In wireless communication, multiple versions of the transmitted signals arrive at the receiver with different attenuations and time delays. Thus, an equalizer with long filter is required at the receiver to reverse the multipath effects. In this paper;, a new non-causal delayless subband equalizer structure is proposed to reduce the computational complexity of the equalizer and to improve the channel tracking capability. This structure avoids the additional delays usually associated with the subband schemes. A design method is formulated to minimize the aliasing effect of the filter bank in the subbands. The significance of employing a non-causal equalizer is also emphasized. Simulation results show that the performance of the new structure is significantly improved by using the design method. Moreover, the complexity of the new subband equalizer is a fraction of the equivalent fullband at the expense of small degradation in the performance.

We consider a class of FIR filters defined by the first order difference routing digital filter (DRDF) structure and sums of two powers-of-two coefficients. A novel design method is developed for constructing high quality filters with reference to the min-max error criterion. This method is highly efficient in terms of computational time. Simulation studies show a large improvement over existing methods such as quantization. In some cases, the peak ripple magnitude over the stop and pass bands is reduced by up to 13 dB over the quantization method. These results are achieved even for cases involving small number of delays.