Effects Of Reverberation Research Articles

Neuroanatomy, episodic memory and inhibitory control of persian-kurdish simultaneous bilinguals

We assessed simultaneous bilinguals and monolinguals on inhibitory control and episodic memory, and assessed their grey matter volumes in brain regions known to be involved in language processing, executive control and memory. Bilinguals outperformed monolinguals on episodic memory, and performance on the memory and inhibition tasks were correlated, only in the bilingual group. This suggests that the bilingualism-related benefits on memory are related to individual differences in executive control. We found larger grey matter volumes in bilinguals in left pars opercularis and in bilateral SFG, caudate nuclei, and parasubiculum. Episodic memory performance was correlated with volumes of bilateral posterior hippocampi, again only in the bilinguals, again suggesting that bilingualism may be driving this effect. Finally, we found positive structural covariance between the volumes of the bilateral parasubiculum and that of important components of the executive control network. We provide a novel, mechanistic explanation accounting for observed behavioural advantage and brain structural differences: bilingualism may boost the prefrontal cortex-hippocampal neural circuitry commonly underlying both executive control and memory, via cascade and reverberant effects, leading to synergistic benefits in both cognitive domains. This new framework has important implications for protective effects on cognition and brain health in relation to second language learning.

Estimating direction of arrival in reverberant environments for wake-word detection using a single structural vibration sensora).

The vibrational response of an elastic panel to incident acoustic waves is determined by the direction-of-arrival (DOA) of the waves relative to the spatial structure of the panel's bending modes. By monitoring the relative modal excitations of a panel immersed in a sound field, the DOA of the source may be inferred. In reverberant environments, early acoustic reflections and the late diffuse acoustic field may obscure the DOA of incoming sound waves. Panel microphones may be especially susceptible to the effects of reverberation due to their large surface areas and long-decaying impulse responses. An investigation into the effect of reverberation on the accuracy of DOA estimation with panel microphones was made by recording wake-word utterances in eight spaces with reverberation times (RT60s) ranging from 0.27 to 3.00 s. The responses were used to train neural networks to estimate the DOA. Within ±5°, DOA estimation reliability was measured at 95.00% in the least reverberant space, decreasing to 78.33% in the most reverberant space, suggesting an inverse relationship between RT60 and DOA accuracy. Experimental results suggest that a system for estimating DOA with panel microphones can generalize to new acoustic environments by cross-training the system with data from multiple spaces with different RT60s.

Sound source localization via distance metric learning with regularization

Sound source localization (SSL) or simply direction of arrival (DOA) classification is an important ingredient in acoustic applications. Traditional model-based algorithms are susceptible to the effects of noise and reverberation, while data-driven deep learning algorithms maintain strong performance across a variety of acoustic circumstances, but typically require a large amount of labeled data. Nevertheless, the existing datasets for SSL are not sufficiently big and diverse to achieve the full potential of deep learning algorithms. Then, it is an imperative work to develop a non-data-hungry algorithm of SSL using small or medium data volume. To this end, we propose a regularized distance metric learning algorithm, that is, by means of the kernel method, we design a nonlinear feature transformation from two aspects: feature points and feature distributions. It transforms the data into a new feature space that brings features of the same class as close as possible and removes features of different classes as far away as possible, which can significantly improve the output of a DOA classifier that follows. Experimental results show that the proposed algorithm outperforms deep learning algorithms in diverse acoustic conditions.

Impact of Reverberation on Speech Perception in Noise in Bimodal/Bilateral Cochlear Implant Users with and without Residual Hearing.

(1) Background: The aim of the present study was to assess the impact of reverberation on speech perception in noise and spatial release from masking (SRM) in bimodal or bilateral cochlear implant (CI) users and CI subjects with low-frequency residual hearing using combined electric-acoustic stimulation (EAS). (2) Methods: In total, 10 bimodal, 14 bilateral CI users and 14 EAS users, and 17 normal hearing (NH) controls, took part in the study. Speech reception thresholds (SRTs) in unmodulated noise were assessed in co-located masker condition (S0N0) with a spatial separation of speech and noise (S0N60) in both free-field and loudspeaker-based room simulation for two different reverberation times. (3) Results: There was a significant detrimental effect of reverberation on SRTs and SRM in all subject groups. A significant difference between the NH group and all the CI/EAS groups was found. There was no significant difference in SRTs between any CI and EAS group. Only NH subjects achieved spatial release from masking in reverberation, whereas no beneficial effect of spatial separation of speech and noise was found in any CI/EAS group. (4) Conclusions: The subject group with electric-acoustic stimulation did not yield a superior outcome in terms of speech perception in noise under reverberation when the noise was presented towards the better hearing ear.

Analyzing the MFCC and GFCC to Identify Reverberation Effects on The Sound

Analyzing the MFCC and GFCC to Identify Reverberation Effects on The Sound

Coherence-based phonemic analysis on the effect of reverberation to practical automatic speech recognition

Reverberation is one of the most critical obstacles to adopt automatic speech recognition (ASR) in real life environments. Therefore, comprehensive understanding on the effect of reverberation to ASR is required to design robust ASR systems for practical uses. To deepen our understanding on the effect of reverberation to practical ASR, we performed a phonemic analysis on commercial ASR system. The analysis method involves a new metric named mean phoneme coherence (MPC), defined by time–frequency-averaged coherence function between clean and reverberated speech spectrograms of each phoneme. MPC measures the amount of spectral contamination on phonemes under certain reverberation condition thus quantifies not only the amount of reverberation on the phonemes but also contextual influences on the phoneme within sentence spoken in the reverberation condition. MPC was proven to represent the amount of reverberation and intelligibility of speeches under given reverberation condition by comparing MPC with word error rate (WER) in real reverberation conditions. Furthermore, the relationship between phoneme groups’ vulnerability to spectral contamination and ASR performance upon reverberation is analyzed by comparing median of phoneme groups’ MPC distribution with phoneme group word accuracy (PGWA). Analysis has shown that the two quantities show weak correlation, thus reverberation differently affects the intelligibility of phonemes. In addition, a comparative study among phoneme groups has shown that nasals and semivowels show the least robust ASR performances to reverberation while nasals and stops are most vulnerable to cause spectral contamination. The results and discussions present what should be taken into account for ASR robust to reverberation.

Rapid kilonova evolution: Recombination and reverberation effects

Kilonovae (KNe) are one of the fastest types of optical transients known, cooling rapidly in the first few days following their neutron-star merger origin. We show here that KN spectral features go through rapid recombination transitions, with features due to elements in the new ionisation state emerging quickly. Due to time-delay effects of the rapidly expanding KN, a ‘wave’ of these new features passing though the ejecta should be a detectable phenomenon. In particular, isolated line features will emerge as blueshifted absorption features first, gradually evolving into P Cygni features and then pure emission features. In this analysis, we present the evolution of individual exposures of the KN AT2017gfo observed with VLT/X-shooter, which together comprise X-shooter’s first epoch spectrum (1.43 days post-merger). The spectra of these ‘sub-epochs’ show a significant evolution across the roughly one hour of observations, including a decrease in the blackbody temperature and photospheric velocity. The early cooling is even more rapid than that inferred from later photospheric epochs and suggests that a fixed power-law relation between the temperature and time does not describe the data. The cooling constrains the recombination wave, where a Sr II interpretation of the AT2017gfo ∼1 μm feature predicts both a specific timing for the feature emergence and its early spectral shape, including the very weak emission component observed at about 1.43 days. This empirically indicates a strong correspondence between the radiation temperature of the blackbody and the ejecta’s electron temperature. Furthermore, this reverberation analysis suggests that temporal modelling is important for interpreting individual spectra and that higher-cadence spectral series, especially when concentrated at specific times, can provide strong constraints on KN line identifications and the ejecta physics. Given the use of such short-timescale information, we lay out improved observing strategies for future KN monitoring.

Individual Differences Elucidate the Perceptual Benefits Associated with Robust Temporal Fine-Structure Processing.

The auditory system is unique among sensory systems in its ability to phase lock to and precisely follow very fast cycle-by-cycle fluctuations in the phase of sound-driven cochlear vibrations. Yet, the perceptual role of this temporal fine structure (TFS) code is debated. This fundamental gap is attributable to our inability to experimentally manipulate TFS cues without altering other perceptually relevant cues. Here, we circumnavigated this limitation by leveraging individual differences across 200 participants to systematically compare variations in TFS sensitivity to performance in a range of speech perception tasks. TFS sensitivity was assessed through detection of interaural time/phase differences, while speech perception was evaluated by word identification under noise interference. Results suggest that greater TFS sensitivity is not associated with greater masking release from fundamental-frequency or spatial cues, but appears to contribute to resilience against the effects of reverberation. We also found that greater TFS sensitivity is associated with faster response times, indicating reduced listening effort. These findings highlight the perceptual significance of TFS coding for everyday hearing.

Patterns of Causative Faults of Normal Earthquakes in the Fluid‐Rich Outer Rise of Northeastern Japan, Constrained With 3D Teleseismic Waveform Modeling

AbstractAccurate earthquake source parameters are crucial for understanding plate tectonics, yet, it is difficult to determine these parameters precisely for offshore events, especially for outer‐rise earthquakes, as the limited availability of direct P or S wave data sets from land‐based seismic networks and the unsuitability of simplified 1D methods for the complex 3D structures of subducting systems. To overcome these challenges, we employ an efficient hybrid numerical simulation method to model these 3D structural effects on teleseismic P/SH and P‐coda waves and determine the reliable centroid locations and focal mechanisms of outer‐rise normal‐faulting earthquakes in northeastern Japan. Two M6+ events with reliable locations from ocean bottom seismic observations are utilized to calibrate the 3D velocity structure. Our findings indicate that 3D synthetic waveforms are sensitive to both event location, thanks to bathymetry and water reverberation effects, and the shallow portion of the lithospheric structure. With our preferred velocity model, which has Vs ∼16% lower than the global average, event locations are determined with uncertainties of <5 km for horizontal position and <1 km for depth. The refined event locations in a good match between one of the nodal strikes and the high‐resolution bathymetry, enabling the determination of the causative fault plane. Our results reveal that trench‐ward dipping normal faults are more active, with three parallel to the trench as expected, while five are associated with the abyssal hills. The significant velocity reduction in the uppermost lithosphere suggests abundant water migrating through active normal faults, enhancing both mineral alteration and pore density.

Static and moving minimum audible angle: Independent contributions of reverberation and position.

Two measures of auditory spatial resolution, the minimum audible angle and the minimum audible movement angle, have been obtained in a simulated acoustic environment using Ambisonics sound field reproduction. Trajectories were designed to provide no reliable cues for the spatial discrimination task. Larger threshold angles were found in reverberant compared to anechoic conditions, for stimuli on the side compared to the front, and for moving compared to static stimuli. The effect of reverberation appeared to be independent of the position of the sound source (same relative threshold increase) and was independently present for static and moving sound sources.

Effect of reverberation and informational masking on speech perception by adolescents and young adults with cochlear implants

Children with severe to profound hearing loss fitted with cochlear implants (CIs) can develop speech with degraded auditory inputs. Little is known about how speech perception by CI users who developed speech through electrical hearing is affected by realistic reverberation—a source of additional degradation in acoustic inputs to the CI speech processor. Moreover, how these young CI users handle competing maskers with high informational masking, such as two-talker babble, in speech-in-noise perception is not well characterized. Both reverberation and competing maskers are relevant factors in everyday classroom listening scenarios. In this work, we studied a cohort of adolescents and young adults with early fitting of CIs. Speech reception thresholds (SRTs) were measured in five types of maskers: speech-shaped noise (SSN), SSN modulated by a speech envelope, two-talker babble in English and in French, and time-reversed two-talker English babble. SRTs from each masker type were repeated in an anechoic condition and two reverberation conditions mimicking a classroom meeting classroom acoustics standard versus a lecture hall. When compared with a group of age-matched normal-hearing listeners, young CI users showed steeper increase of masked SRTs by increasing reverberation but comparable release from informational masking. [Work supported by the Hearing Health Foundation.]

Building a real-time convolution system for assessing vocal health of teachers

Due to prevalent vocal health issues in teachers, the acoustics of K-12 classrooms has become a common topic of study in acoustics. One way to understand the impact of a classroom’s acoustics on speech is through real-time convolution of speech with a binaural room impulse response (BRIR). This is done by having a talker seated in an anechoic chamber and their vocal effort can be assessed while using the real-time convolution system to simulate the acoustics of a variety of classroom conditions. Keeping the talker in one physical space provides more control over the testing environment. A system that can successfully execute convolution in real-time requires parameters to be fine-tuned, an optimized algorithm, and appropriate hardware. Current efforts and lessons learned during the development of this system are shared. Goals for a finished real-time convolution system include specific testing to determine the effects of background noise and reverberation on a teacher’s vocal effort.

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs.

This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice.

Assessing acoustical quality in university classrooms in Brazil: Measurements and simulations

The acoustic quality of learning spaces, such as classrooms, is a worldwide issue. Some countries have regulations or guidelines for these environments, which does not happen properly in Brazil. Beyond the lack of nationwide research related to classroom acoustics, Brazil has two standards with a few information related to classroom acoustics. One of them was written in 1992, having a single demand concerning an appropriate reverberation time for conference rooms at the 500 Hz frequency, without detailed requirement, in contrast to other national guidelines. Another one, updated in 2020, recommends the sound pressure levels for indoor environments, having classrooms in its list. The aim of this study is to analyze the acoustic quality of two Brazilian classrooms over a public university in the south part of Brazil. Several impulse responses were taken within the learning environments and then processed in Matlab with the ITA ToolBox. The classrooms were also modeled into RAVEN and I-SIMPA software aiming to identify different sound absorption schemes to improve their speech intelligibility. The findings showed that the classrooms had a bad acoustic environment, with a measured reverberation time of approximately [Formula: see text]. However, the simulation suggested that implementing sound absorption strategies could reduce the reverberation effects by half, bringing it closer to optimal levels. The estimated costs for implementing sound absorbers were [Formula: see text] for Classroom 01 and [Formula: see text] for Classroom 02.

The design, characterization, and performance assessment of an aeroacoustic measurement system for a hard-walled wind tunnel

A new aeroacoustic measurement system for the large wind tunnel (LWT) at the University of New South Wales (UNSW) has been designed and characterized. The LWT is a closed-return type wind tunnel with a 2.87m × 1.28 m × 0.92 m hard-walled test-section, which contains 0.61m × 0.61 m access windows one of which was used to attach a 1.06m × 0.61 m × 0.61 m anechoic chamber lined with 100 mm thick Basotect foam. A 16-microphone spiral phased array embedded in the chamber was used to characterize the acoustical properties of the chamber and localize several acoustic and aeroacoustic sources placed in the reverberant test-section. Acoustic sources considered include single and dual speaker setup, while a UAV propeller and a forward-backward step mounted on the test-section sidewall were used as an aeroacoustic source. The beamforming output in each case was deconvolved using several existing algorithms to assess their performance under reflective boundary conditions. Additionally, a numerical tailored Greens function approach to beamforming is being considered to exclude the effects of reverberations within the test-section from the array output. It is expected that the present work will help develop strategies and guidelines for aero/hydroacoustic testing under reverberant conditions.

Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios.

A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI scenarios. The system is evaluated with a telephone dataset re-recorded using the robotic platform designed and implemented for this study. In addition, the original telephone training data are modified using an environmental model that incorporates natural robot-generated and external noise sources and reverberant effects using room impulse responses (RIRs). The results indicate that the average accuracy and AUC are just 0.4% less than those obtained with matched training/testing conditions with simulated data. Quite surprisingly, there is not much difference in accuracy and AUC between static and dynamic HRI conditions. Moreover, the beamforming methods delay-and-sum and MVDR lead to average improvement in accuracy and AUC equal to 8% and 2%, respectively, when applied to training and testing data. Regarding the complementarity of time-dependent and time-independent features, the combination of both types of classifiers provides the best joint accuracy and AUC score.

Densely Connected Networks with Multiple Features for Classifying Sound Signals with Reverberation.

In indoor environments, reverberation can distort the signalseceived by active noise cancelation devices, posing a challenge to sound classification. Therefore, we combined three speech spectral features based on different frequency scales into a densely connected network (DenseNet) to accomplish sound classification with reverberation effects. We adopted the DenseNet structure to make the model lightweight A dataset was created based on experimental and simulation methods, andhe classification goal was to distinguish between music signals, song signals, and speech signals. Using this framework, effectivexperiments were conducted. It was shown that the classification accuracy of the approach based on DenseNet and fused features reached 95.90%, betterhan the results based on other convolutional neural networks (CNNs). The size of the optimized DenseNet model is only 3.09 MB, which is only 7.76% of the size before optimization. We migrated the model to the Android platform. The modified model can discriminate sound clips faster on Android thanhe network before the modification. This shows that the approach based on DenseNet and fused features can dealith sound classification tasks in different indoor scenes, and the lightweight model can be deployed on embedded devices.

An acoustic tracking model based on deep learning using two hydrophones and its reverberation transfer hypothesis, applied to whale tracking

Acoustic tracking of whales’ underwater cruises is essential for protecting marine ecosystems. For cetacean conservationists, fewer hydrophones will provide more convenience in capturing high-mobility whale positions. Currently, it has been possible to use two hydrophones individually to accomplish direction finding or ranging. However, traditional methods only aim at estimating one of the spatial parameters and are susceptible to the detrimental effects of reverberation superimposition. To achieve complete whale tracking under reverberant interference, in this study, an intelligent acoustic tracking model (CIAT) is proposed, which allows both horizontal direction discrimination and distance/depth perception by mining unpredictable features of position information directly from the received signals of two hydrophones. Specifically, the horizontal direction is discriminated by an enhanced cross-spectral analysis to make full use of the exact frequency of received signals and eliminate the interference of non-source signals, and the distance/depth direction combines convolutional neural network (CNN) with transfer learning to address the adverse effects caused by unavoidable acoustic reflections and reverberation superposition. Experiments with real recordings show that 0.13 km/MAE is achieved within 8 km. Our work not only provides satisfactory prediction performance, but also effectively avoids the reverberation effect of long-distance signal propagation, opening up a new avenue for underwater target tracking.

The Effects of Noise and Reverberation Time on Auditory Sustained Attention

The indoor acoustic environment is very important for auditory learning, and the completion of various auditory learning tasks cannot be separated from auditory sustained attention. Noise and reverberation time (RT) are the two most common acoustic indicators that affect attention. Based on this, experiments on auditory sustained attention under two factors of noise masking and reverberation effect alone were conducted in an anechoic chamber and a reverberation chamber, respectively. In addition, nine kinds of RT and signal-to-noise ratios (SNR) (3 RT × 3 SNRs) were also designed and implemented in the paper, and the impact of RT and noise on the perception of auditory sustained attention was analyzed through the above three experimental scenarios. The experimental results show the following: (1) noise type has no significant effect on auditory sustained attention performance, and SNR has a significant effect on omission errors (OEs), commission errors (CEs), and mean reaction time (MRT). All three decline with the increase in SNR; when SNR reaches a certain level, it tends to stabilize, and according to the experimental results, good auditory sustained attention SNR needs at least 18 dB; (2) The effect of RT on auditory sustained attention perception has significant differences. As RT increases, the number of inattentive OE increases, the number of impulses CE increases, and the response time becomes longer; (3) SNR has a significant negative impact on college students’ auditory sustained attention performance, while RT has a less negative effect compared to SNR; (4) The functional relationships between auditory sustained attention performance and SNR and RT in three scenarios were obtained, which can be used to predict inattentive OE, impulse, and MRT in noisy and reverberant environments. This study is the first attempt to explore the influence of noise and RT on the auditory sustained attention of Chinese university students, providing theoretical support for the acoustic design of indoor learning environments, and the findings can fill the gap of the lack of basic experimental data in the current standards of the indoor acoustic environment in China.

The Effect of Noise and Reverberation on Spatial Perception in Sequential Bilateral Cochlear Implant Users.

Spatial orientation is an executive function which includes vital activities and auditory organization according to daily bodily movements, directionality, and environmental information. It is directly linked to the vision and hearing and used throughout life, building complex relationships with these systems, based on learning. Our purpose in our study is to try to see the effects of noise and reverberation on the users by comparing the localization and auditory performances of the cochlear implant (CI) user individuals in a silent, noisy environment and reverberation. All subjects were subjected to immitancemetric/audiological tests, language development test (TIFALDI, Receptive/Expressive Language Test score 7 years and above), localization determination in noise, and localization determination test in reverberation. Study sample: In our study, 18 female and 16 male bilateral CI users with profound sensorineural hearing loss were included. The age range of subjects was 8 years 4 months and 10 years 11 months. Data from subjects were collected prospectively. Data analysis was analyzed with SPSS 21 program. It was observed that the subjects did not have difficulty in determining the direction in silent condition, but they had a significant difficulty in localizing the 135-, 225-, and 315-degree angles especially when the noise was signal-to-noise ratio (SNR) -10 dB and the reverberation was 06 and 09 second (p ≤ 0.005). Subjects' performances were significantly altered in sequential implanted users both when the SNR was changed and in the presence of reverberation (p < 0.05). As a result of our study, it is thought that individuals with hearing loss will experience intense difficulties, especially in noisy and reverberant environments such as schools, and using assistive listening devices in these environmental conditions will contribute positively to their academic development.