Oxidative aging induces significant stiffening of asphalt binders that leads to a pronounced reduction in the overall durability of asphalt pavements. The strategic implementation of antioxidant additives provides a potential solution to alleviate this issue. This work presents results from the second phase of the global consortium for antioxidants research aimed at investigating the effectiveness of potential antioxidants in increasing the durability of asphalt binders. Sixteen laboratories around the world participated in this effort and a total of 28 binders from diverse geographical regions were tested. Two promising antioxidants, namely zinc diethyldithiocarbamate (ZDC) and kraft lignin were evaluated in this phase and blended with the binders at specific proportions. Subsequently, a comprehensive investigation was conducted to assess rheological characteristics and chemical properties of the various blends, utilising Dynamic Shear Rheometer (DSR) measurements and Fourier Transform Infrared (FTIR) Spectroscopy. The findings indicate that additives such as ZDC hold considerable promise as an effective antioxidant, particularly when considering a wide diversity of binders. In general, its incorporation does not compromise the rutting performance of the binders and significantly improves fatigue performance. Therefore, research efforts should be focused on exploring additional facets to assess its practical applicability in field.

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Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges. In the past 10 years, the pavement infrastructure strongly supports the rapid development of the global social economy. New theories, new methods, new technologies and new materials related to pavement engineering are emerging. Deterioration of pavement infrastructure is a typical multi-physics problem. Because of actual coupled behaviors of traffic and environmental conditions, predictions of pavement service life become more and more complicated and require a deep knowledge of pavement material analysis. In order to summarize the current and determine the future research of pavement engineering, Journal of Traffic and Transportation Engineering (English Edition) has launched a review paper on the topic of “New innovations in pavement materials and engineering: A review on pavement engineering research 2021”. Based on the joint-effort of 43 scholars from 24 well-known universities in highway engineering, this review paper systematically analyzes the research status and future development direction of 5 major fields of pavement engineering in the world. The content includes asphalt binder performance and modeling, mixture performance and modeling of pavement materials, multi-scale mechanics, green and sustainable pavement, and intelligent pavement. Overall, this review paper is able to provide references and insights for researchers and engineers in the field of pavement engineering.

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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.

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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.

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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.

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While the majority of binaural beamformers aim to minimize the output noise power while (approximately) preserving the binaural cues of the sources using constraints, we propose in this paper to minimize the binaural-cue distortions of the sources in the acoustic scene, such that the output noise power is below a predefined threshold. This new problem formulation is a convex QCQP problem, which leads to an efficient trade-off between noise reduction, binaural-cue preservation and complexity. In particular, the proposed beamformer provides a better trade-off between noise reduction and binaural-cue preservation (in terms of interaural level and phase differences) compared to the well-known binaural minimum variance distortionless response-η beamformer.

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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.

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In this paper, we perceptually evaluate two recently proposed binaural multi-microphone speech enhancement methods in terms of intelligibility improvement and binaural-cue preservation. We compare these two methods with the well-known binaural minimum variance distortionless response (BMVDR) method. More specifically, we measure the 50% speech reception threshold, and the localization error of all dominant point sources in three different acoustic scenes. The listening tests are divided into a parameter selection phase and a testing phase. The parameter selection phase is used to select the algorithms' parameters based on one acoustic scene. In the testing phase, the two methods are evaluated in two other acoustic scenes in order to examine their robustness. Both methods achieve significantly better intelligiblity compared to the unprocessed scene, and slightly worse intelligibility than the BMVDR method. However, unlike the BMVDR method which severely distorts the binaural cues of all interferers, the new methods achieve localization errors which are not significantly different compared to those of the unprocessed scene.@en

Binaural beamformers (BFs) aim to reduce the output noise power while simultaneously preserving the binaural cues of all sources. Typically, the latter is accomplished via
constraints relating the output and input interaural transfer functions (ITFs). The ITF is a function of the corresponding relative acoustic transfer function (RATF), which implies that RATF estimates of all sources in the acoustic scene are required. Here, we propose an alternative way to approximately preserve the binaural cues of the entire acoustic scene without estimating RATFs. We propose to preserve the binaural cues of all sources with a set of fixed pre-determined RATFs distributed around the head. Two recently proposed binaural BFs are evaluated in the context of using pre-determined RATFs and compared to the binaural minimum variance distrortionless response BF which can only preserve the binaural cues of the target.@en

Binaural multi-microphone noise reduction methods aim at noise suppression while preserving the spatial impression of the acoustic scene. Recently, a new binaural speech enhancement method was proposed which chooses per timefrequency (TF) tile either the enhanced target or a suppressed noisy version. The selection between the two is based on the input SNR per TF tile. In this paper we modify this method such that the selection mechanism is based on the output SNR. The proposed modification of deciding which TF tile is target-or
noise-dominated leads to choices, which are better aligned with simultaneous masking properties of the auditory system, and, hence, improves the performance over the initial version of the algorithm.@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