Beamforming is a common technique used to improve speech intelligibility and listening comfort of hearing aids users in a noisy environment. Traditional hearing aids beamforming algorithms require the a priori knowledge of the auditory of the listener, which may not be available in real applications. Recent advances in electroencephalography (EEG) offer a potential non-invasive solution to this problem. The listener's auditory is derived from the EEG signals through auditory decoding algorithms and can be used as an input to the beamforming algorithms. In [1], a joint auditory decoding and adaptive beamforming algorithm framework by correlating the envelope of beamforming output and the EEG signal was proposed to improve the beamformer's robustness against decoding error. Consistent performance improvement was demonstrated on an EEG database recorded on listeners with fixed . In this study, we present the evaluation results of this joint formulation on a new EEG dataset collected on subjects with dynamic switch. We demonstrate not only the joint framework's performance improvement against decoding errors, but also its ability to capture listener's dynamic switch.

Traditional adaptive binaural beamforming algorithms for hearing devices often assume that the target talker is known or can be derived from the listener's look direction. When this assumption is violated, the traditional beamforming algorithms often produce distorted target speech and less than optimal noise and interference suppression. Recent advances in electroencephalography (EEG) and its applications to auditory attention decoding have offered a potential solution for tracking the listeners auditory attention in a multi-talker environment [2]-[5]. In this paper, we propose a unified model for joint auditory attention decoding and adaptive binaural beamforming, and solve the problem using an iterative optimization approach. The proposed algorithm has two advantages over the existing algorithms. First, the optimization objective aims to balance auditory attention alignment, target speech distortion, noise and interference suppression. Secondly, there is no need to estimate the speech envelope of each talker from the noisy and reverberant mixture which is a very challenging problem in practice. The proposed algorithm was evaluated using a newly recorded EEG database for a multi-talker, noisy and reverberant environment [6]. The evaluation results confirm the benefits of the proposed algorithm.

Beamforming is a common technique used to improve speech intelligibility and listening comfort of hearing aids users in a noisy environment. Traditional beamforming algorithms such as linearly constrained minimum variance (LCMV) beamformer cannot effectively suppress multiple interferences when the degree of freedom (DoF) of the array is less than the number of sources in the environment. In [1], a penalized inequality-constrained minimum variance (P-ICMV) beamformer was proposed to address this challenge. In this study, we evaluate the P-ICMV beamformer and compare its performance with other beamformers including the LCMV in a multiple-interference environment. In an objective evaluation, objective metrics related to speech intelligibility and sound quality are used to compare the algorithm performance. In a subjective evaluation, the speech intelligibility of the beamformer processed stimuli are evaluated using normal-hearing listeners. Both the objective and subjective evaluation results show that the P-ICMV beamformer can suppress the interferences more effectively than the existing beamformers when the array DoF is limited.

In this paper, a novel algorithm named Sparsity-based Wiener plus Dictionary Learning (SWDL) is proposed for single channel speech enhancement. SWDL combines both Wiener filter and dictionary learning technique. The Wiener filter is used to ensure the enhanced speech is statistically optimal, while the dictionary learning technique is used to improve the enhanced speech quality and intelligibility by utilizing speech-specific information. Such information is incorporated in the pre-trained speech dictionary that can sparsely represent the clean speech spectra. When applied to the TIM-IT database, SWDL outperforms the Log Mean Square-Error Short-Time Spectra Amplitude estimator (LSTSA) according to four different objective metrics measuring speech quality and intelligibility. Subjective tests also show that SWDL produces better speech quality and intelligibility than LSTSA.

In this paper, we consider the single-channel speech enhancement problem, in which a clean speech signal needs to be estimated from a noisy observation. To capture the characteristics of both the noise and speech signals, we combine the well-known Short-Time-Spectrum-Amplitude (STSA) estimator with a machine learning based technique called Multi-frame Sparse Dictionary Learning (MSDL). The former utilizes statistical information for denoising, while the latter helps better preserve speech, especially its temporal structure. The proposed algorithm, named STSA-MSDL, outperforms standard statistical algorithms such as the Wiener filter, STSA estimator, as well as dictionary based algorithms when applied to the TIMIT database, using four different objective metrics that measure speech intelligibility, speech distortion, background noise reduction, and the overall quality.

Perceptual annoyance of environmental sounds is measured for normal-hearing and hearing-impaired listeners under iso-level and iso-loudness conditions. Data from the hearing-impaired listeners shows similar trends to that from normal-hearing listeners, but with greater variability across individuals. A regression model based on the statistics of specific loudness and other perceptual features is fit to the data from the normal-hearing listeners, and is used to predict annoyance for the hearing-impaired listeners. Differences across the subject populations are discussed.

This paper presents an efficient implementation of a robust adaptive beamforming algorithm based on convex optimization for applications in the processing-constrained environment of a digital hearing aid. Several modifications of the standard interior point barrier method are introduced for use where the array data covariance matrix is changing rapidly relative to the algorithm's convergence rate. These efficiency improvements significantly simplify the computation without affecting the algorithm's fast convergence, and are useful for real-time adaptive beamforming regardless of the rate of array correlation change. Simulation results show that this implementation is numerically stable and succeeds where many minimum-variance distortionless response (MVDR) solutions fail.

In many imaging applications, the continuous phase information of the measured signal is wrapped to a single period of 2pi, resulting in phase ambiguity. In this paper we consider the two-dimensional phase unwrapping problem and propose a maximum a posteriori (MAP) framework for estimating the true phase values based on the wrapped phase data. In particular, assuming a joint Gaussian prior on the original phase image, we show that the MAP formulation leads to a binary quadratic minimization problem. The latter can be efficiently solved by semidefinite relaxation (SDR). We compare the performances of our proposed method with the existing L1/L2-norm minimization approaches. The numerical results demonstrate that the SDR approach significantly outperforms the existing phase unwrapping methods.

Controlling the polarization information in transmitted waveforms enables improving the performance of radar systems. We consider the design of optimal polarization for the estimation of target scattering embedded in heavy clutter. The goal is to minimize the mean squared error of the scattering estimation subject to an average radar pulse power constraint. We show that such a problem is equivalent to the optimal design of a radar sensing matrix that contains the polarization information. We formulate the optimal design as a nonlinear optimization problem which can be recast it in a convex form and is thus efficiently solvable by semi-definite programming (SDP). We compare the sensing performance of the optimally selected polarization over the conventional approaches. Our numerical results indicate that a significant amount of power gain is achieved in the target scattering estimation through such an optimal design.

We consider decentralized estimation of a noise-corrupted deterministic parameter using a bandwidth-constrained sensor network with a fusion center (FC). Each sensor's noise is additive, zero mean, and independent across sensors. A decentralized estimator is said to be universal if the local sensor quantization rules and the final fusion rule at the FC are independent of sensor noise pdf. Assuming that information rate from each sensor to the FC is constrained to one bit per sample, we derive a Crameacuter-Rao lower bound (CRLB) on the mean-squared error (MSE) performance of a class of rate-constrained universal decentralized estimators. Our results show that if sensor observation noise has finite range in [-U,U], then the minimum MSE performance of any one-bit rate-constrained universal decentralized estimator is at least U2/(4K), where K is the total number sensors. This bound implies that the recently proposed universal decentralized estimators are optimal up to a constant factor of 4

We consider the problem of multiterminal source-channel communication where a number of distributed and possibly correlated sources are transmitted through an orthogonal multiple access channel to a common destination. We provide a characterization of the optimal tradeoff between the transmission cost Gamma and the distortion vectorDas measured against individual sources. Our approach consists of two steps: 1) a multiple-letter characterization of the rate-distortion region of the multiterminal source coding and 2) a source-channel separation theorem ensuring that all achievable pairs of (Gamma,D) can be obtained by combining the rate-distortion region and the orthogonal multiple access channel capacity region. As a corollary, we determine the optimal power and distortion tradeoff in a quadratic Gaussian sensor network under orthogonal multiple access, and show that separate source and channel coding strictly outperforms the uncoded (amplify-forward) transmission, and is in fact optimal in this case. This result is in sharp contrast to the case of nonorthogonal multiple access for which separate source and channel coding is not only suboptimal but also strictly inferior to uncoded transmission.

We consider the distributed estimation of an unknown vector signal in a bandwidth constrained sensor network with a fusion center (FC). Due to power and bandwidth limitations, each sensor compresses its data in order to minimize the amount of information that needs to be communicated to the FC. In this context, we design a linear decentralized estimation scheme (DES), where each sensor linearly encodes its observations before the transmission to the FC, which performs a minimum mean squared error (MMSE) estimation for the unknown vector signal based on the received messages. When the channels between sensors and the FC are orthogonal, it has been shown previously that the complexity of designing the optimal encoding matrices is NP-hard in general. In this paper, we study the optimal design of linear DES for the case of non- orthogonal multiple access channel (MAC) under both bandwidth and power constraints. We show that when the MAC between sensors and the FC is noiseless, the resulting problem has a closed-form solution, while in the noisy MAC case, the problem can be efficiently solved by semi-definite programming (SDP).

We consider the linear coding of a discrete memoryless Gaussian source transmitted through a discrete memoryless fading channel with additive white Gaussian noise (AWGN). The goal is to minimize the mean squared error (MSE) of the source reconstruction at the destination subject to an average power constraint imposed on the channel input symbols. We show that among all single-letter (or symbol-by-symbol) codes, linear coding achieves the smallest MSE, and is thus optimal. But when block length increases, the linear coding still shares the same performance with the single-letter coding, and thus can not approach the Shannon's bound. In spite of the suboptimality, the performance loss of linear coding compared to the optimal coding can be quantitively bounded in terms of the variance of the fading gain and the average transmit power. We also show that for linear coding, when there is no transmitter channel state information (CSI), uniform power allocation is optimal, and in the presence of transmitter CSI, the optimal power allocation can be analytically solved in terms of the channel fading gains and the average power budget.

We investigate distributed estimation based on measurements from multiple wireless sensors. For the same target, different sensors have different observations, which are modeled by additive observation noises of different variances. The observations are transmitted using (analog) amplify-and-forward transmissions from the sensors over non-ideal wireless channels to a fusion center, where they are combined to generate an estimate of the observed target. Our goal is to minimize total end-to-end distortion under certain power constraints, assuming the Best Linear Unbiased Estimator (BLUE) is used. We analyze the system outage performance, and show an achievable diversity gain of order K, which is the number of sensors. We also show that by turning off bad sensors, i.e., sensors with bad channels, we achieve adaptive power gain without losing diversity gain, where the adaptive power gain is similar to the array gain achieved in Multiple Input Single Output (MISO) systems when channel conditions are known to the transmitter.

Sensor networks in which energy is a limited resource so that energy consumption must be minimized for the intended application are considered. In this context, an energy-efficient method for the joint estimation of an unknown analog source under a given distortion constraint is proposed. The approach is purely analog, in which each sensor simply amplifies and forwards the noise-corrupted analog observation to the fusion center for joint estimation. The total transmission power across all the sensor nodes is minimized while satisfying a distortion requirement on the joint estimate. The energy efficiency of this analog approach is compared with previously proposed digital approaches with and without coding. It is shown in our simulation that the analog approach is more energy-efficient than the digital system without coding, and in some cases outperforms the digital system with optimal coding.

We consider the problem of a single parameter estimation by a sensor network with a fusion center (FC). Sensor observations are corrupted by additive noise which can have arbitrary spatial correlation. Due to a bandwidth constraint each sensor is only able to transmit a finite number of bits. The fusion center combines messages from the sensors to produce a parameter estimator, which is required to have mean square error (MSE) within a constant factor of that of the best linear unbiased estimator (BLUE). We show that total sensor transmitted power can be minimized while meeting target MSE requirement if quantization levels are determined jointly by the fusion center using the knowledge of the noise covariance matrix. By numerical examples we show that energy saving up to 70% can be achieved when compared to a uniform quantization strategy when each sensor generates the same number of bits.

We consider universal decentralized estimation of a noise-corrupted signal by a bandwidth constrained sensor network with a fusion center (FC). We show that in a homogeneous sensing environment and under a bandwidth constraint of 1-bit per sample per node, there exist universal decentralized estimation schemes (DES) with a mean squared error (MSE) decreasing at the rate 1/K, where K is the total number of sensors. We extend such 1-bit decentralized estimators to the case of a inhomogeneous sensing environment, and propose quantization and transmission power control strategies for local sensors in order to minimize the total consumed sensor energy while ensuring a given MSE performance. We also design a DES for the joint estimation of a vector source based on its noisy and linearly distorted observations, and show that to achieve a MSE within a factor of 2 away from the best linear unbiased estimator (BLUE), the local message length has a nice form of being the channel capacity of "a virtual AWGN channel" from "nature" to each local sensor.

We consider decentralized estimation of a noise-corrupted deterministic parameter by a bandwidth-constrained sensor network with a fusion center. The sensor noises are assumed to be additive, zero mean, spatially uncorrelated, but otherwise unknown and possibly different across sensors due to varying sensor quality and inhomogeneous sensing environment. The classical best linear unbiased estimator (BLUE) linearly combines the real-valued sensor observations to minimize the mean square error (MSE). Unfortunately, such a scheme cannot be implemented in a practical bandwidth-constrained sensor network due to its requirement to transmit real-valued messages. In this paper, we construct a decentralized estimation scheme (DES) where each sensor compresses its observation to a small number of bits with length proportional to the logarithm of its local signal-to-noise ratio (SNR). The resulting compressed bits from different sensors are then collected and combined by the fusion center to estimate the unknown parameter. The proposed DES is universal in the sense that each sensor compression scheme requires only the knowledge of local SNR, rather than the noise probability distribution functions (pdf), while the final fusion step is also independent of the local noise pdfs. We show that the MSE of the proposed DES is within a constant factor of 25/8 of that achieved by the classical centralized BLUE estimator.

Consider the problem of decentralized detection with a distributed sensor network where the communication channels between sensors and the fusion center are bandlimited. Previous approaches to this problem typically rely on quantization of either the sensor observations, or the local likelihood ratios, with quantization levels optimally designed using the knowledge of noise distribution. We assume that each sensor is restricted to send a 1-bit message to the fusion center, and that sensor noise is additive, zero mean, spatially independent, but otherwise unknown and with possibly different distributions across sensors. We construct a universal decentralized detector using a recently proposed isotropic decentralized estimation scheme (Z.-Q. Luo, 2003) which requires only the knowledge of either the noise range or its second order moment. We show that the error probability of this detector decays exponentially at a rate which is optimal for bounded range noise, and, for noise with unbounded range, is lower bounded in terms of the signal-to-noise ratio.

We consider the decentralized estimation of a noise-corrupted deterministic parameter by a bandwidth constrained sensor network with a fusion center. We construct a decentralized estimation scheme (DES) where each sensor compresses its observation to a small number of bits with length proportional to the logarithm of its local Signal to Noise Ratio (SNR). The resulting compressed bits from different sensors are then collected and combined by the fusion center to estimate the unknown parameter. The proposed DES is universal in the sense that the local sensor compression schemes and final fusion function are independent of noise pdf. We show that its mean squared error is within a constant factor to that achieved by the classical centralized best linear unbiased estimator (BLUE).

We consider the problem of optimal power scheduling for the decentralized estimation of a noise-corrupted signal in an inhomogeneous sensor network. Sensor observations are first quantized into discrete messages, then transmitted to the fusion center where a final estimate is generated. Based on the sensor noise levels and channel gains from sensors to the fusion center, optimal quantization levels and transmit power levels at the local sensors can be chosen to minimize the total transmitting power, while ensuring a given mean squared error (MSE) performance. The proposed optimal power scheduling scheme suggests that the sensors with bad channels or poor observation qualities should decrease their quantization resolutions or simply become inactive in order to conserve power. For the remaining active sensors, their optimal quantization and transmit power levels are determined jointly by individual channel gains, local observation noise variance, and the targeted MSE performance. Numerical examples show that up to 60% energy savings is possible when compared with the uniform quantization strategy.