Wireless acoustic sensor networks (WASNs) can be used for centralized multi-microphone noise reduction, where the processing is done in a fusion center (FC). To perform the noise reduction, the data needs to be transmitted to the FC. Considering the limited battery life of the devices in a WASN, the total data rate at which the FC can communicate with the different network devices should be constrained. In this article, we propose a rate-constrained multi-microphone noise reduction algorithm, which jointly finds the best rate allocation and estimation weights for the microphones across all frequencies. The optimal linear estimators are found to be the quantized Wiener filters, and the rates are the solutions to a filter-dependent reverse water-filling problem. The performance of the proposed framework is evaluated using simulations in terms of mean square error and predicted speech intelligibility. The results show that the proposed method is very close in performance to that of the existing optimal method based on discrete optimization. However, the proposed approach can do this at a much lower complexity, while the existing optimal reference method needs a non-tractable exhaustive search to find the best rate allocation across microphones.@en

Compared to monaural hearing aids (HAs), binaural hearing aid systems, in which there is a communication link between the two devices, have improved noise reduction capabilities and the ability to preserve binaural spatial information. However, the limited HA battery lifetime puts constraints on the amount of information that can be shared between the two devices. In other words, the rate of transmission between the devices is an important constraint that needs to be considered, while preserving the spatial information. In this article, a linearly constrained noise reduction problem is proposed, which jointly finds the optimal rate allocation and the optimal estimation (beamforming) weights across all sensors and frequencies, while preserving the binaural spatial cues of point sources. The proposed method considers a rate constraint together with linear constraints to preserve the binaural spatial cues of point sources. Minimizing the mean square error on the estimated target speech at the left and the right side beamformers, the optimal weights are found to be rate-constrained linearly constrained minimum variance (LCMV) filters, and the optimal rates are found to be the solutions to a set of reverse water filling problems. The performance of the proposed method is evaluated using the averaged binaural signal-to-noise ratio (SNR), the interaural level difference (ILD) error and the interaural time difference (ITD) error. The results show that the proposed method outperforms spatially correct noise reduction approaches that use naive/random rate allocation strategies.@en

Multi-microphone noise reduction algorithms in binaural hearing aids which cooperate through a wireless link have the potential to become of great importance in future hearing aid systems. However, limited transmission capacity of such devices necessitates the data compression of signals transmitted from one hearing aid to the contralateral one. In this paper we study the impact of quantization as a data compression scheme on the performance of the multi-microphone noise reduction algorithms. Using the binaural minimum variance distortionless response (BMVDR) beamformer as an illustration, we propose a quantization aware beamforming scheme which uses a modified cross power spectral density (CPSD) of the system noise including the quantization noise (QN). Moreover, several assumptions on the QN are investigated in the proposed method. Based on the output SNR, we compare different variations of the proposed method with the conventional BMVDR beamformer. The results confirm the improved performance of the proposed method.@en

Binaural hearing aids (HAs) can potentially perform advanced noise reduction algorithms, leading to an improvement over monaural/bilateral HAs. Due to the limited transmission capacities between the HAs and given knowledge of the complete joint noisy signal statistics, the optimal rate-constrained beamforming strategy is known from the literature. However, as these joint statistics are unknown in practice, sub-optimal strategies have been presented. In this paper, we present a unified framework to study the performance of these existing optimal and sub-optimal rate-constrained beamforming methods for binaural HAs. Moreover, we propose to use an asymmetric sequential coding scheme to estimate the joint statistics between the microphones in the two HAs. We show that under certain assumptions, this leads to sub-optimal performance in one HA but allows to obtain the truly optimal performance in the second HA. Based on the mean square error distortion measure, we evaluate the performance improvement between monaural beamforming (no communication) and the proposed scheme, as well as the optimal and the existing sub-optimal strategies in terms of the information bit-rate. The results show that the proposed method outperforms existing practical approaches in most scenarios, especially at middle rates and high rates, without having the prior knowledge of the joint statistics.@en

Modern binaural hearing aids (HAs) can collaborate wirelessly with each other as well as with other assistive (wireless) devices. This enables multi-microphone noise reduction over small wireless acoustic sensor networks (WASNs) to increase the intelligibility under adverse conditions. In this work, we assume one of the HAs to serve as the fusion center (FC). The optimal beamforming strategy for processing the received data at the FC depends on the acoustic scene and physical constraints (e.g., the bit-rate for transmission to the FC), and might be frequency dependent. Selection of the optimal beamforming strategy, while satisfying rate constraints on the communication between the different devices is an important challenge in such setups. In this paper, we propose an operational rate-constrained beamforming system for optimal rate allocation and strategy selection across frequency. We show an example of the proposed framework, where both the algorithm selection as well as the required rates to transmit the necessary microphone signals are optimized using uniform quantizers, while minimizing the mean-square error (MSE) distortion measure. In contrast to a well-known (theoretically optimal) reference method based on remote source coding for two devices, the presented algorithm is practically implementable and only requires knowledge of joint signal statistics at the FC. Evaluations (based on simulation experiments) show clear improvement over other practically implementable strategies.@en

Many people around the world suffer from hearing problems (In the Netherlands, around 11%of the population is considered hearing-impaired). To overcome their hearing problems, advanced technologies like hearing aid devices can be used. Hearing aids are meant to assist the hearing-impaired to improve the speech intelligibility and the quality of sounds that they intend to hear. Usually these include processors which are mainly designed to enhance the sound signals originating from the source of interest by reducing the environmental noise. Binaural hearing aids, on the other hand, can also help to preserve some spatial information from the acoustic scene, which can help the hearing aid user to hear the sounds from the correct locations. To construct the binaural hearing aid system, two hearing aids are needed to be placed in the left and the right ears, which can potentially communicate through a wireless link. In addition, one can think of additional assisting devices with microphones placed in the environment. One common way to reduce the noise is to use advanced binaural multi-microphone noise reduction algorithms, which aim at estimating some desired sources while reducing the power of the undesired sources. One typical method is to use spatial filtering, which aims at estimating the target signal by shaping the beam towards the location of the desired source while canceling/suppressing the other sources. To perform binaural noise reduction, while assuming centralized processing, the signals recorded at remote microphones (for example from additional assisting devices or in the binaural hearing aid setup, the sound signals from the contralateral hearing aid) need to be transmitted to the central processor. Due to the power and bandwidth limitations, the data needs to be compressed before transmission. Therefore, the main question would be, at which rate the data should be compressed to have reasonably good noise reduction performance. This links the noise reduction problem to the data compression problem. Generally, the higher the data rate, the better the noise reduction performance. Therefore, there is a trade-off between the performance of the noise reduction algorithm and the data-rate at which the information is compressed. This problem is closely connected to the rate-distortion problem from an information-theoretic viewpoint. Studying the effect of data compression on the performance of noise reduction problems would be of great interest to reduce the power consumption of hearing assistive devices. Oneway to incorporate data compression into the noise reduction problem is to perform quantization, which leads to a rate-constrained noise reduction problem. In the rate-constrained noise reduction, the goal is to estimate the desired sources based on the imperfect data. The observations from remote sensors are quantized and transmitted to the fusion center. The main challenge in the binaural rate-constrained noise reduction is to find the best quantization rates for the different sensors at different frequencies, given the physical constraints like bitrate and power constraints. Another aspect of the rate-constrained noise reduction is to expand the network to receive more information on the acoustic scene using additional assistive devices. Target source estimation using information from such assistive devices (rather than only binaural hearing aids) is shown to result in better noise reduction performance. Now the question is how to allocate the bitrates to the assistive devices as well. These assistive devices can be thought of as the remote embedded microphones on the cell-phones (mobile) or wearable microphones placed at the users’ bodies. The binaural hearing aid system can thus be generalized to allow other assistive devices to contribute to noise reduction. In this dissertation, we study and propose different rate-constrained multi- microphone noise reduction algorithms. We try to expand the notion of the binaural rate constrained noise reduction to multi-microphone rate-constrained noise reduction for general wireless acoustic sensor networks (WASNs). The WASN in this case can include the binaural setup along with other assistive devices. We propose different algorithms to cover the main objectives of rate-constrained noise reduction problems. These objectives mainly include good target estimation (less environmental noise power) given the compressed data, good rate allocation strategies in WASNs, and preferably preserved spatial information of the sources in the acoustic scene to get the correct impression of the acoustic scene. @en