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Abstract: This paper addresses the problem of automatic speech recognition in real applications in which the speech signal is altered by various noises. Feature compensation and model compensation robustness methods are studied. Parallel model combination (PMC) and its recent advances are reviewed and a novel algorithm called PC-PMC is proposed. This algorithm utilizes cepstral mean subtraction (CMS) normalization ability and principal component analysis (PCA) compression and de-correlation capability in the combination with PMC model transformation method. PC-PMC algorithm takes the advantages of additive noise compensation ability of PMC and convolutional noise removal capability of CMS and PCA. In realizing PC-PMC mainly two problems should be solved. The first problem is that PMC method requires invertible modules in the front–end of the system while CMS normalization is not an invertible process. Moreover, when the recognition system is exposed to noisy speech, the adaptation of the PCA transform is required; therefore a framework is to be designed for adaptation of the PCA transform in the presence of noise. The method presented in this paper provides solutions to these problems. Our evaluations are done on the four different real noisy tasks using Nevisa HMM-based, Persian continuous speech recognition system. Experimental results demonstrate significant reduction in the system word error rate using PC-PMC. In addition, the effects of covariance matrix compensation, dynamic features adaptation and the effect of gain parameter in the PMC method are also studied and experimented in the real acoustic conditions. Besides, we have investigated the using of maximum likelihood linear regression (MLLR) and maximum a posteriori (MAP) adaptation techniques in the combination with the introduced PC-PMC method. Finally, a comprehensive discussion on the capabilities of the studied robustness methods is presented. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers connections between the cost functions of some parameter identification methods for system modelling, including the well known projection algorithm, stochastic gradient (SG) algorithm and recursive least squares (RLS) algorithm, and presents a modified SG algorithm by introducing the convergence index and a multi-innovation projection algorithm, a multi-innovation SG algorithm and a multi-innovation RLS algorithm by introducing the innovation length, aiming at improving the convergence rate of the SG and RLS algorithms. Furthermore, this paper derives an interval-varying multi-innovation SG and an interval-varying multi-innovation RLS algorithm in order to deal with missing data cases. [Copyright &y& Elsevier]

Abstract: The array signal processing problem has to do with two main issues: 1) the determination of source number; and 2) estimation of the directions-of-arrival (DOA) of these sources. In the classical methods, the determination of source number and the estimation of DOA parameters are executed independently rather than jointly. To jointly estimate source number and DOA, this paper proposes a simulated annealing-based algorithm. The key points of this paper are: i) the prior choices for some unknown parameters are calibrated to compute more efficiently; ii) a penalty term for the parameter number is added to the corresponding objective function Additionally to avoid overfitting; and iii) in order to move to the regions of interest quickly, some cumulants of properly chosen sensor outputs from the uniform linear array (ULA) is computed and their moduluses of Fourier transform (MFT) are mapped trickly to the uniform distribution. Finally simulation results are presented to validate the performance of the proposed method. [Copyright &y& Elsevier]

In this paper, we proposed an effective and computationally efficient algorithm without iterations, named general sparse denoising with total variation regularization (GSDN-TV), for solving the convex optimization problem of combining the sparse regularization and total variation (TV) regularization. In the GSDN-TV, the original convex optimization problem is divided into two convex optimization subproblems. Each of the subproblems only contains one regularization and can be efficiently solved or has the closed-form solution. The final solution of the original problem can be obtained by solving the two subproblems one by one without iterations. By using the non-convex firm penalty function in the sparse regularization, the GSDN-TV is applied to the wavelet-TV denoising problem and achieves outstanding performances. [ABSTRACT FROM AUTHOR]

In this paper, a novel method based on the entropy estimation of the observation space eigenvalues is proposed to estimate the number of independent sources impinging on a sensor array. In this method we do not need to know a priori information about the noise model and we can use it in any Gaussian or non-Gaussian model of observations and noise. Our analytical results show that the proposed algorithm is consistent and an approximation for probability of false alarm and an upper bound for probability of missed detection are derived analytically. The performance of the proposed algorithm is compared with the existing methods in the presence of Gaussian and non-Gaussian noise via the simulations. It is shown that this information theoretic method called EEE, has a better performance than those methods in the literature, especially in non-Gaussian noise environment. [ABSTRACT FROM AUTHOR]

Determining the frequencies of multiple resolvable exponentials is an important problem due to its application in diverse areas in science and engineering. In this paper, frequency estimation of two-dimensional (2-D) sinusoids is addressed. With the use of the periodogram in frequency domain, the required harmonics are first located coarsely. The characteristics of the 2-D spectrum is then analyzed, and the accurate estimates of the parameters are retrieved using an interpolation method iteratively. It is proved that at sufficiently high signal-to-noise ratio conditions, the harmonic estimates are asymptotically unbiased, and their variances are also analyzed. Furthermore, when only part of the data is observed, the proposed algorithm is tailored to get fast and accurate estimation results. Computer simulations are also included to compare the proposed approach with conventional 2-D harmonic retrieval schemes in terms of root mean square error performance and computational complexity. [ABSTRACT FROM AUTHOR]

Simultaneous sparse approximation is a generalization of the standard sparse approximation, for simultaneously representing a set of signals using a common sparsity model. Distributed compressive sensing (DCS) framework has utilized simultaneous sparse approximation for generalizing compressive sensing to multiple signals. DCS finds the sparse representation of multiple correlated signals from compressive measurements using the common + innovation signal model. However, DCS is limited for joint recovery of a large number of signals since it requires large memory and computational time. In this paper, we propose a new hierarchical algorithm to implement the joint sparse recovery part of DCS more efficiently. The proposed approach is based on partitioning the input set and hierarchically solving for the sparse common component across these partitions. The numerical evaluation of the proposed method shows the decrease in computational time over DCS with an increase in reconstruction error. The proposed algorithm is evaluated for two different applications. In the first application, the proposed method is applied to video background extraction problem, where the background corresponds to the common sparse activity across frames. In the second application, a common network structure is extracted from dynamic functional brain connectivity networks. [ABSTRACT FROM AUTHOR]

The discrete cosine transform (DCT) has been successfully used for a wide range of applications in digital signal processing. While there are efficient algorithms for implementing the DCT, its use becomes difficult in the sliding transform scenario where the transform window is shifted one sample at a time and the transform process is repeated. In this paper, a new two-dimensional sliding DCT (2-D SDCT) algorithm is proposed for fast implementation of the DCT on 2-D sliding windows. In the proposed algorithm, the DCT coefficients of the shifted window are computed by exploiting the recursive relationship between 2-D DCT outputs of three successive windows. The theoretical analysis shows that the computational requirement of the proposed 2-D SDCT algorithm is the lowest among existing 2-D DCT algorithms. Moreover, the proposed algorithm enables independent updating of each DCT coefficient. [ABSTRACT FROM AUTHOR]

To improve end-to-end throughput and to reduce signaling overhead in multi-hop relay networks, broadcast virtual multiple-input and multiple-output (MIMO) systems (BVMSs) have been introduced. Conventionally, this research has been done for a limited environment where each node is equipped with a single-antenna and also interference from other networks is not included for the numerical analysis. In this paper, we propose a new virtual MIMO broadcasting transceiver (VMBT) to overcome the limitation of conventional BVMS and to improve end-to-end throughput for BVMS-based multi-hop-relay networks while the signaling overhead effectively reduced. Toward this goal, proposed VMBT is designed based on the following contributions: analysis of the channel ellipse property, convergence proof of the iterative algorithm and utilization of the null and span of channel vectors. The simulation results show that the proposed VMBT achieves the highest end-to-end throughput compared with that of other conventional technologies. [ABSTRACT FROM AUTHOR]

Wireless Body area networks (WBANs) consist of sensors that continuously monitor and transmit real time vital signals to a nearby coordinator and then to a remote terminal via the Internet. One of the most important signals for monitoring in WBANs is the electrocardiography (ECG) signal. The design of an accurate and energy efficient ECG telemonitoring system can be achieved by: i) reducing the amount of data that should be transmitted ii) minimizing the computational operations executed at any transmitter/receiver in a WBAN. To this end, compressed sensing (CS) approaches can offer a viable solution. In this paper, we propose two novel CS based ECG reconstruction algorithms that minimize the samples that are required to be transmitted for an accurate reconstruction, by exploiting the block structure of the ECG in the time domain (TD) and in an uncorrelated domain (UD). The proposed schemes require the solutions of second-order cone programming (SOCP) problems that are usually tackled by computational demanding interior point (IP) methods. To solve these problems efficiently, we develop a path-wise coordinate descent based scheme. The reconstruction accuracy is evaluated by the percentage root-mean-square difference (PRD) metric. A reconstructed signal is acceptable if and only if PRD < 9 % . Simulation studies carried out with real electrocardiographic (ECG) data, show that the proposed schemes, operating in both the TD and in the UD as compared to the conventional CS techniques, reduce the Compression Ratio (CR) by 20 % and 44 % respectively, offering at the same time significantly low computational complexity. [ABSTRACT FROM AUTHOR]

Abstract: This paper shows a new algorithm to improve the performance of IQ demodulators and frequency converters exhibiting gain and phase imbalances between their branches. This algorithm does not require any input calibration signal and is independent of the input signal level. It exploits the spectral coherence (SC) concept using a monobit kernel to achieve optimization targets with minimum time to convergence, low computational load, and a wide range of input levels. Its effectiveness is shown through a low-IF receiver that improves its image rejection ratio (IRR) from 30 dB to 60 dB. [Copyright &y& Elsevier]

Abstract: Blind source separation (BSS) has attained much attention in signal processing society due to its ‘blind’ property and wide applications. However, there are still some open problems, such as underdetermined BSS, noise BSS. In this paper, we propose a Bayesian approach to improve the separation performance of instantaneous mixtures with non-stationary sources by taking into account the internal organization of the non-stationary sources. Gaussian mixture model (GMM) is used to model the distribution of source signals and the continuous density hidden Markov model (CDHMM) is derived to track the non-stationarity inside the source signals. Source signals can switch between several states such that the separation performance can be significantly improved. An expectation-maximization (EM) algorithm is derived to estimate the mixing coefficients, the CDHMM parameters and the noise covariance. The source signals are recovered via maximum a posteriori (MAP) approach. To ensure the convergence of the proposed algorithm, the proper prior densities, conjugate prior densities, are assigned to estimation coefficients for incorporating the prior information. The initialization scheme for the estimates is also discussed. Systematic simulations are used to illustrate the performance of the proposed algorithm. Simulation results show that the proposed algorithm has more robust separation performance in terms of similarity score in noise environments in comparison with the classical BSS algorithms in determined mixture case. Additionally, since the mixing matrix and the sources are estimated jointly, the proposed EM algorithm also works well in underdetermined case. Furthermore, the proposed algorithm converges quickly with proper initialization. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a novel image adaptive technique for high capacity watermarking scheme using accurate and fast radial harmonic Fourier moments (RHFMs). The high embedding capacity is achieved by improving the hiding ratio after reducing inaccuracies in the computation of RHFMs. The binary watermark is embedded by performing the conditional quantization of selected RHFMs magnitudes to minimize the spatial distortion added to the host image. In addition, fast algorithms based on 8-way symmetry/anti-symmetry properties and recursive relations for the computation of sinusoidal kernel functions are adopted to enhance the speed of RHFMs-based watermarking process. Experimental studies show that the proposed watermarking scheme provides higher embedding capacity, good visual imperceptibility, better robustness to geometric distortions and common signal processing transformations, and lower computational complexity compared to the existing Zernike and pseudo-Zernike moments (ZMs/PZMs)-based watermarking schemes. [Copyright &y& Elsevier]

Abstract: This paper presents a new time domain noise reduction approach based on Singular Value Decomposition (SVD) technique. In the proposed approach, the noisy signal is initially represented in a Hankel Matrix. Then SVD is applied on the Hankel Matrix to divide the data into signal subspace and noise subspace. Since singular vectors are the span bases of the matrix, reducing the effect of noise from the singular vectors and using them in reproducing the matrix leads to considerable enhancement of information embedded in the matrix. The noise-reduced singular vectors from the signal subspace are utilized to reconstruct the data matrix. This matrix is finally used to obtain the time-series signal. The results of applying the proposed method to different synthetic noisy signals indicate a better efficiency in noise reduction compared to the other time series methods. [Copyright &y& Elsevier]

Abstract: A major drawback of many speech enhancement methods in speech applications is the generation of an annoying residual noise with musical character. Although the Wiener filter introduces less musical noise than spectral subtraction methods, such noise, however, exists and is perceptually annoying to the listener. A potential solution to this artifact is the incorporation of a psychoacoustic model in the suppression filter design. In this paper a frequency domain optimal linear estimator with perceptual post-filtering is proposed, which incorporates the masking properties of the human hearing system to render the residual noise distortion inaudible. Proposed post-processing presents a modified way to measure the tonality coefficient and relative threshold offset for an optimal estimation of the noise masking threshold. The performance of the proposed enhancement algorithm is evaluated by the segmental SNR, Modified Bark Spectral Distortion (MBSD) and Perceptual Evaluation of Speech Quality (PESQ) measures under various noisy environments and yields better results compared to the Wiener filtering based on Ephraim–Malah''s decision-directed approach. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new Empirical Mode Decomposition based algorithm for the purpose of QRS complex detection. This algorithm requires the following stages: a high-pass filter, signal Empirical Mode Decomposition, a nonlinear transform, an integration and finally, a low-pass filter is used. In order to evaluate the proposed technique, the well known ECG MIT–BIH database has been used. Moreover it is compared to a reference technique, namely “Christov''s” detection method. As it will be shown later, the proposed algorithm allows to achieve high detection performances, described by means both the sensitivity and the specificity parameters. [Copyright &y& Elsevier]

Abstract: This paper proposes a new non-parametric method to estimate the power spectral density of atmospheric radar signals using uniform filter banks through polyphase approach. The performance of proposed method is investigated for a simulated broadband and narrowband signals. This method reduces the variance without affecting the resolution and improves the signal to noise ratio significantly when compared to the existing method (periodogram/modified periodogram). The variance reduction is observed to be more in case of narrowband signals. The method has the computational cost similar to modified periodogram approach. This method is tested for practical atmospheric data collected at NARL, Gadanki, India, through the mesosphere–stratosphere–troposphere (MST) radar, back scattered echoes, in particular from high altitudes, which has low signal to noise ratio. Results have been validated using simultaneous Global Positioning System (GPS) Sonde data. [Copyright &y& Elsevier]

Abstract: This paper uses an estimated noise transfer function to filter the input–output data and presents filtering based recursive least squares algorithms (F-RLS) for controlled autoregressive autoregressive moving average (CARARMA) systems. Through the data filtering, we obtain two identification models, one including the parameters of the system model, and the other including the parameters of the noise model. Thus, the recursive least squares method can be used to estimate the parameters of these two identification models, respectively, by replacing the unmeasurable variables in the information vectors with their estimates. The proposed F-RLS algorithm has a high computational efficiency because the dimensions of its covariance matrices become small and can generate more accurate parameter estimation compared with other existing algorithms. [Copyright &y& Elsevier]

Abstract: It has been found that envelopes established by extrema in the empirical mode decomposition cannot always depict the local characteristics of a signal very well. This is due in part to the slight oscillations characterized as hidden scales which are almost left untreated during the sifting process. When involving hidden scales, the intrinsic mode function usually contains at a given instance multiple oscillation modes. In view of this, based on inflection points this paper presents a new decomposition algorithm called ‘oblique-extrema empirical mode decomposition’ to settle these problems. With this algorithm, any signal can be decomposed into a finite number of ‘oblique-extrema intrinsic mode functions’ which may possess better-behaved Hilbert transforms and produce more accurate instantaneous frequencies. It can suppress the effect of hidden scales and gets one step further in extracting finer scales. Experimental results demonstrate good performances of this new method. [Copyright &y& Elsevier]

Abstract: The implementation of adaptive filters with fixed-point arithmetic requires computation quality evaluation. The accuracy may be determined by computing the global quantization noise power at the system output. In this paper, a new model for evaluating analytically the global noise power in LMS-based algorithms is presented. Thus, the model is developed for LMS and NLMS algorithms. The accuracy of our model is analyzed by simulations. [Copyright &y& Elsevier]

Abstract: Recently, a number of underdetermined blind source separation (UBSS) approaches have been proposed to separate n source signals from m () mixtures. The majority of these methods depend on the assumption that the source signals are sparse in either time domain or other transformation domains. While the sparsity assumption is reasonable for some applications, such as the separation of speech signals, it is restrictive for many other applications. In this paper, we propose a new time-frequency UBSS algorithm without resort to any sparsity conditions. We show that under some mild constraints, our algorithm can separate up to sources from m () instantaneous linear mixtures. Theoretical analysis and simulation results show the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, a particle swarm optimization (PSO) is used to design the quadrature mirror filter (QMF) banks with linear phase in frequency domain. A unique PSO is developed to optimize filter bank coefficients to match the ideal system response. The three main attributes used in assessing the performance of filter are reconstruction error, mean square error in pass band and mean square error in stop band. The proposed PSO based method produces almost the same result as that of the existing methods. As compared to the existing methods this method is very simple to implement for the QMF bank optimization. To implement the proposed PSO algorithm, a Matlab program is developed and three examples have been presented to illustrate the performance of the proposed method. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to present an FPGA-oriented system design for ECG signal processing. The system performs QRS complex detection and beat classification into either normal or premature ventricular contractions. QRS complex detection is made with an algorithm based on a phase-space portrait of an ECG signal, while for beat classification a part of the Open Source ECG Analysis Software is used. The former is performed in HW and the latter in SW on an integrated PowerPC 405 core. The algorithm was developed on the MIT-BIH Arrhythmia Database where a QRS complex sensitivity of 99.82% and positive predictivity of 99.83% and a PVC sensitivity of 92.36% and specificity of 95.54% were achieved. The processing speed is measured and the results show that the HW/SW implementation of the algorithm processes the ECG data up to 15 times faster than the SW implementation, meaning that real-time ECG signal analysis is possible at much lower frequencies than in analysis performed in SW. We show that the HW/SW implementation makes it possible to use low-cost FPGA devices with soft-core microprocessors in systems where a high processing speed is required. [Copyright &y& Elsevier]

Abstract: This paper presents the development and implementation of a variable rate time-scaling expansion system for speech signals, based on the pitch information, in which only the voiced segments are expanded, keeping the unvoiced and silence segments unchanged. The proposed system was first evaluated by computer simulation and then implemented on a digital signal processor (DSP). Time-domain, frequency-domain, mean opinion score (MOS) and diagnostic rhyme test (DRT) evaluations were done to test the actual performance of developed algorithm, which show that the proposed system allows improving the learning level of foreign language students as well as the understanding ability of elderly people. Objective tests also were carried out in order to probe similarity between the original and the expanded signals. Applying an iterative refinement of the C source code it was possible to obtain a real-time implementation. The current implemented algorithm requires 11 kwords program memory and about 9 million of floating point operations per second (MFLOPS). [Copyright &y& Elsevier]

Britton, D. F., and Smith, M. J. T., Generalized Gaussian Decompositions for SAR Enhancement Using Analysis-by-Synthesis, Digital Signal Processing12 (2002) 284–294Quality enhancement algorithms for synthetic aperture radar (SAR) images offer the potential to increase the accuracy of analysis and threat assessment. This paper presents a new SAR image enhancement method for that purpose. The method is built around a Gaussian representation of the image using analysis-by-synthesis as the engine for the approximation. Its sequential structure and synthesis property enables object boundaries and speckle characteristics to be decoupled and modified to enhance the quality. Evaluations demonstrate that the algorithm helps improve the performance of automatic target recognition systems as well as enhance subjective appearance. [Copyright &y& Elsevier]