The overall level had little impact on performance for either NH or HI listeners. The results demonstrate that the MRT80 test could be a useful test to assess the distortion effects of hearing loss on speech intelligibility, particularly in cases where it is desirable to use a closed-set test for automatic administration.Estimation of the clean speech short-time magnitude spectrum (MS) is key for speech enhancement and separation. Moreover, an automatic speech recognition (ASR) system that employs a front-end relies on clean speech MS estimation to remain robust. Training targets for deep learning approaches to clean speech MS estimation fall into three categories computational auditory scene analysis (CASA), MS, and minimum mean square error (MMSE) estimator training targets. The choice of the training target can have a significant impact on speech enhancement/separation and robust ASR performance. Motivated by this, the training target that produces enhanced/separated speech at the highest quality and intelligibility and that which is best for an ASR front-end is found. Three different deep neural network (DNN) types and two datasets, which include real-world nonstationary and coloured noise sources at multiple signal-to-noise ratio (SNR) levels, were used for evaluation. Ten objective measures were employed, including the word error rate of the Deep Speech ASR system. It is found that training targets that estimate the a priori SNR for MMSE estimators produce the highest objective quality scores. Moreover, it is established that the gain of MMSE estimators and the ideal amplitude mask produce the highest objective intelligibility scores and are most suitable for an ASR front-end.A method for measuring in situ compressional wave attenuation exploiting the spectral decay of reflection coefficient Bragg resonances is applied to fine-grained sediments in the New England Mud Patch. Measurements of layer-averaged attenuation in a 10.3 m mud layer yield 0.04 0.03, 0.055 dB/m/kHz (braces indicate outer bounds); the attenuation is twice as large at a site with 3.2 m mud thickness. It is shown that both results are heavily influenced by a ∼1 m sand-mud transition interval created by geological and biological processes that mix sand (at the base of the mud) into the mud. Informed by the observations, it appears that the spatial dependence of mud layer attenuation across the New England Mud Patch can be predicted by accounting for the transition interval via simple scaling. Further, the ubiquity of the processes that form the transition interval suggests that the scaling may be applied to any muddy continental shelf. In principle, attenuation predictions in littoral environments could be substantively improved with a modest amount of geologic and biologic information.This paper deals with the question of how specific weather conditions affect the perception of aircraft noise. Auralization is a suitable method by enabling parametrical decompositions of the overall aircraft noise scenario into source and propagation components. Considering influences on the auditory perception, the signal processing chain contains different virtual receivers and post processing using psychoacoustic hearing models. For broad coverage, generic standardized as well as measurement-based atmosphere models with variation of ground impedances such as soil data are evaluated. These variations are given aircraft noise measurement values based on A-weighted sound pressure levels LA and psychoacoustic measures regarding loudness, N, and sharpness, S. The results show an immense influence of weather conditions on A-weighted sound pressure levels and on psychoacoustic perception of aircraft noise, too. The weather-dependent differences of A-weighted sound pressure levels are up to 15 dBA and relative differences regarding loudness of factor 1.6 and sharpness of factor 2.0 occur. The approach can be used to get a better understanding of how the temporal statistics of specific local weather conditions and their perceptual consequences may lead to improved taxation of actual noise events and to an improved basis for long-term averages of aircraft noise effects.This paper proposes a multiple signal classification (MUSIC) framework for direction-of-arrival estimation by combining multiple circular arrays in the circular-harmonics domain. We jointly transform the received signals of all sub-arrays into the circular harmonics domain to generate a set of sound field coefficients containing the direction information of the sound sources. These coefficients are then formulated in a form in which MUSIC algorithm can be applied. Compared with the conventional circular-harmonics-domain localization methods, which are based on a single circular array, the proposed method can provide sufficient spatial resolution over different frequency ranges by adjusting the distribution of sub-arrays. Furthermore, the mean square error of the estimated sound field coefficients is derived for guiding this adjustment. Numerical simulation results indicate that a reasonable distribution of sub-arrays can effectively avoid the performance degradation caused by the zeros of Bessel functions, which is an inherent problem of the modal array signal processing. Simulation and experimental results with different configuration parameters demonstrate that the proposed method provides a better localization performance compared to the state-of-the-art methods.Active mechanisms that regulate cochlear gain are hypothesized to influence speech-in-noise perception. However, evidence of a relationship between the amount of cochlear gain reduction and speech-in-noise recognition is mixed. Findings may conflict across studies because different signal-to-noise ratios (SNRs) were used to evaluate speech-in-noise recognition. Also, there is evidence that ipsilateral elicitation of cochlear gain reduction may be stronger than contralateral elicitation, yet, most studies have investigated the contralateral descending pathway. The hypothesis that the relationship between ipsilateral cochlear gain reduction and speech-in-noise recognition depends on the SNR was tested. A forward masking technique was used to quantify the ipsilateral cochlear gain reduction in 24 young adult listeners with normal hearing. Speech-in-noise recognition was measured with the PRESTO-R sentence test using speech-shaped noise presented at -3, 0, and +3 dB SNR. Selleck Lenalidomide Interestingly, greater cochlear gain reduction was associated with lower speech-in-noise recognition, and the strength of this correlation increased as the SNR became more adverse.