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In cases with highly non-stationary noise, single-channel speech enhancement is quite challenging, mainly when the noise includes interfering speech. In this situation, deep learning's success has contributed to speech enhancement to boost intelligibility and perceptual quality. Existing speech enhancement (SE) works in time–frequency domains only aim to improve the magnitude spectrum via neural network learnings; the latest research highlights the significance of phase in perceptual speech quality. Motivated by multi-task machines and deep learning this paper, proposes an effective and novel approach to the task of speech enhancement using an encoder-decoder architecture based on Deep Complex Convolutional Neural Networks. The proposed model takes input from the spectrograms of the noisy speech signals, consisting of real and imaginary components for complex spectral mapping, and it simultaneously enhances the magnitude and phase responses of speech. Considering unseen non-stationary noise categories, which interfere with speech, the proposed model enhances speech quality by approximately, 0.44 MOS points compared to state-of-the-art single-stage techniques. Moreover, it outperforms all reference techniques constantly and improves intelligibility under low-SNR settings. In contrast, against the baselines, we find an incredible enhancement of over 3 dB in SNR, and 0.2 in STOI. In addition, our method outperforms baseline SE techniques in low-SNR conditions in terms of STOI. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ascertaining the presence of weakly positive anti‐HLA donor‐specific antibodies (DSA) in organ transplantation with multiplex single antigen beads assays may be challenging despite their high sensitivity due to technical variability issues. Through extensive datasets of Next‐Generation Sequencing HLA typings and single antigen analyses, we reassessed the mean fluorescence intensity (MFI) positivity threshold of the assay to enhance accuracy. By showing that some beads were more prone to false positivity than others, we propose a nuanced approach that accounts for nonspecific intrinsic reactivities at the HLA antigen level, that is, on a bead‐by‐bead basis, as it enhances assay precision and reliability. This is substantiated by a comprehensive statistical analysis of MFI values and the implementation of the determination of a "Quantile Adjusted Threshold 500" (QAT500) value for each bead. Applied to DSA detection during patients' follow‐up, this approach discriminated better and earlier low‐strength DSA that would later raise their MFI above the clinically relevant threshold of 3000. Moving from a subjective interpretation to a more objective and precise methodology allows for standardizing HLA antibody and DSA detection. The study emphasizes the need for further research with real clinical data to validate and refine this approach. [ABSTRACT FROM AUTHOR]

Coherent diffractive imaging with X‐ray free‐electron lasers could enable structural studies of macromolecules at room temperature. This type of experiment could provide a means to study structural dynamics on the femtosecond timescale. However, the diffraction from a single protein is weak compared with the incoherent scattering from background sources, which negatively affects the reconstruction analysis. This work evaluates the effects of the presence of background on the analysis pipeline. Background measurements from the European X‐ray Free‐Electron Laser were combined with simulated diffraction patterns and treated by a standard reconstruction procedure, including orientation recovery with the expand, maximize and compress algorithm and 3D phase retrieval. Background scattering did have an adverse effect on the estimated resolution of the reconstructed density maps. Still, the reconstructions generally worked when the signal‐to‐background ratio was 0.6 or better, in the momentum transfer shell of the highest reconstructed resolution. The results also suggest that the signal‐to‐background requirement increases at higher resolution. This study gives an indication of what is possible at current setups at X‐ray free‐electron lasers with regards to expected background strength and establishes a target for experimental optimization of the background. [ABSTRACT FROM AUTHOR]

ABSTRACT Non‐destructive testing of steel wire rope is significant in lifting machinery. However, it is still challenging to extract weak damage characteristics of steel wire rope under noise caused by harsh working conditions. Currently, extracting damage characteristics mostly involves secondary processing through software, which is cumbersome and inefficient. Stochastic resonance is a typical stochastic dynamic phenomenon of a nonlinear system, and it can extract weak damage characteristics through the cooperation of noise, weak signals, and nonlinear system models. Among different nonlinear system models, the asymmetric monostable system, which has only one fixed point, is advantageous for achieving a more stable response under pulse excitation. In this work, a hardware circuit based on an asymmetric monostable system is constructed to extract weak damage characteristics of steel wire rope. This hardware circuit can efficiently extract damage characteristics without secondary processing. Additionally, the denoising performance of the nonlinear circuit, focusing on weak signals with damage characteristics under a noisy background, is evaluated using the cross‐correlation coefficient and local cross‐correlation coefficient. The results demonstrate that the proposed method provides high recovery in the non‐destructive testing of steel wire rope. [ABSTRACT FROM AUTHOR]

People with hearing loss often encounter difficulties in hearing under adverse conditions, such as listening in the presence of noise. Listening effort is an indicator used to assess listening difficulties in daily life. Although many studies on listening effort have been conducted in recent years, there is a notable gap in the exploration of how task load influences listening effort in young adults. This study compared the effects of background noise and memory load on task performance and subjective listening effort in young adults with and without hearing loss. The study included a group of 8 adults with hearing loss (mean age: 24.1 ± 6.0 years) and a group of 16 individuals with normal hearing (mean age: 27.9 ± 4.9 years). A number memorizing task was conducted, involving two types of auditory digits (either three or seven digits) presented under multi-talker babble noise conditions of signal-to-noise ratio of −5 dB [SN −5 dB] or SN +5 dB. Participants determined whether the number presented in the encoding interval matched the one presented in the retrieval interval. Subsequently, they were asked to complete a questionnaire using a Visual Analog Scale (VAS) to assess their subjective listening effort. Percentage of correct responses, reaction times, and VAS ratings were compared between adults with and without hearing loss. Our results showed significant differences between the two groups in the percentage of correct responses and the reaction time under the SN −5 dB conditions, regardless of the memory load. Under the SN +5 dB conditions, a significant difference was found only in the percentage of correct responses for seven digits. In the normal hearing group, the percentage of correct responses and VAS ratings tended to decrease as the memory load increased, even under the same noise condition. Conversely, in the hearing loss group, a consistent trend could not be identified in the effects of noise and memory load on the percentage of correct responses and VAS ratings. These results suggest that in conditions of high noise load, young adults with hearing loss show a higher tendency for listening effort to be affected by other loads. We confirmed that for some participants with hearing loss, the task exceeded a certain level of difficulty in the SN −5 dB and seven digits condition, leading to a change in their motivation and strategy used. Future research should examine ways to control for participants' motivations. [ABSTRACT FROM AUTHOR]

Depression is a prevalent mental illness that requires autonomous detection systems due to its complexity. Existing machine learning techniques face challenges such as background noise sensitivity, slow adaptation speed, and imbalanced data. To address these limitations, this study proposes a novel ModWave Cepstral Fusion and Stochastic Embedding Framework for depression prediction. Then, the Gain Modulated Wavelet Technique removes background noise and normalises audio signals. Difficulties with generalisation, which results in a lack of interpretability, hinder extracting relevant characteristics from speech. To address these issues, an Auto Cepstral Fusion extracts relevant features from speech, capturing temporal and spectral characteristics caused by background voice. Feature selection becomes imperative when choosing relevant features for classification. Selecting irrelevant features can result in overfitting, the curse of dimensionality, and less robustness to noise. Hence, the Principal Stochastic Embedding technique handles high-dimensional data, minimising noise influence and dimensionality. Furthermore, the XGBoost classifier differentiates between depressed and non-depressed individuals. As a result, the proposed method uses the DAIC-WOZ dataset from USC for detecting depressions, achieving an accuracy of 97.02%, precision of 97.02%, recall of 97.02%, F1-score of 97.02%, RMSE of 2.00, and MAE of 0.9, making it a promising tool for autonomous depression detection.

Classrooms are often noisy environments, which can result in unfavorable learning conditions for students. However, research has insufficiently addressed how noisy classrooms affect autistic students. This preliminary study examined differences in, and the impact of, background noise on reading performance for elementary-aged autistic and non-autistic (NA) children (N = 49). Autistic (n = 13) and NA children (n = 36) between the ages of 6 and 13 years participated in the current study. We employed a repeated measures design where each participant read four, grade-appropriate reading curriculum-based measurement (CBM) passages and subsequently completed comprehension (i.e., retell and recall) tasks in the presence of four different listening conditions (i.e., experimental condition): (1) quiet, (2) a single talker, (3) classroom noise, and (4) white noise. Using multi-level modeling (MLM), we found that listening condition differentially impacted reading fluency for all children. Children's reading fluency was more negatively impacted by the single talker in comparison to white noise and quiet. The performance of all children to retell story components (a measure of reading comprehension) was moderated by age with older children recalling more story components in the presence of white noise. Recalling story components correctly was not impacted by listening condition or disability. Regardless of disability, environments that include a single talker were not optimal for children's reading fluency skills. Moreover, preferred environments for children's reading comprehension skills—specifically the retelling of key story components—depend on student age, with background white noise being ideal for older children. Notably, no differences in how background noise impacts reading performance were found between autistic and NA children. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing methods are insufficient in dealing with the background noise anti-interference of underwater fish images, a contrastive learning method of ignoring background called CLIB for underwater fish image classification is proposed to improve the accuracy and robustness of underwater fish image classification. First, CLIB effectively separates the subject from the background in the image through the extraction module and applies it to contrastive learning by composing three complementary views with the original image. To further improve the adaptive ability of CLIB in complex underwater images, we propose a multi-view-based contrastive loss function, whose core idea is to enhance the similarity between the original image and the subject and maximize the difference between the subject and the background, making CLIB focus more on learning the core features of the subject during the training process, and effectively ignoring the interference of background noise. Experiments on the Fish4Knowledge, Fish-gres, WildFish-30, and QUTFish-89 public datasets show that our method performs well, with improvements of 1.43-6.75%, 8.16-8.95%, 13.1-14.82%, and 3.92-6.19%, respectively, compared with the baseline model, further validating the effectiveness of CLIB. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Heterogeneous audio splicing tampering, which combines audio recordings from different scenarios or devices, has posed a significant challenge to audio authenticity. Most of the existing work is not good at the detection and localization for multiple splicing points, especially when the signal-to-noise ratios (SNRs) of recordings involved in splicing are close. In this work, we propose an audio splicing detection and localization method on the basis of multistage filterbank spectral sketches (MFBSS) and decision fusion. More specifically, we first remove the silent segments to reduce the redundant information and estimate the background noise of the combined voice-only segments. Next, to obtain more audio details, we propose a feature fusion strategy to extract the MFBSS feature from the background noise. Then, we develop a decision fusion strategy to detect and localize all the possible splicing points. Finally, we evaluate our method against the state-of-the-art splicing detection approaches on public datasets with various noises and SNR differences. Experimental results demonstrate that the proposed approach is effective for various noise scenarios with small SNR differences and is also robust against anti-forensics attacks. [ABSTRACT FROM AUTHOR]

Auditory distractions can impair the sensory evaluation of food; however, the specific impact of airplane cabin noise on the sensory perception of in-flight meals remains poorly studied. Here, we investigated the effects of airplane cabin noise on the visual processing of in-flight meal stimuli using electroencephalography (EEG) in twenty healthy male subjects. Resting-state EEG and event-related potential (ERP) responses to in-flight meal images were acquired during quiet and simulated cabin noise conditions. Participants reported mild discomfort and some loss of appetite when exposed to airplane cabin noise. The analysis of resting-state EEG showed an increase in the absolute power of theta and beta frequency bands in the left superior parietal and left frontal/right central regions under simulated cabin noise conditions, compared to quiet conditions. The ERP results showed that the amplitude of responses evoked by visual meal images in the superior parietal area was reduced in the noise condition compared to the quiet condition. Our findings suggest that airplane cabin noise disrupts the visual perception and attentional processing of in-flight food stimuli. These neural changes imply an impact on integrating sensory information, resulting in altered sensory evaluations of food during in-flight dining experiences. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the performance and reliability of deep learning-based speaker recognition schemes under various recording situations and background noise presence. The study uses the Speaker Recognition Dataset offered in the Kaggle website, involving audio recordings from different speakers, and four scenarios with various combinations of speakers. In the first scenario, the scheme achieves discriminating capability and high accuracy in identifying speakers without taking into account outside noise, having roughly one area under the ROC curve. Nevertheless, in the second scenario, with background noise added to the recording, accuracy decreases, and misclassifications increase. However, the scheme still reveals good discriminating power, with ROC areas ranging from 0.77 to 1.

The ambient noise analysis was performed at ten installed broadband seismic stations of the Eastern Dharwar Craton in Telangana state, India. We calculated the power spectral densities and their corresponding probability density functions, using different types of noise to assess the stations' performance. One year of continuous data from January 2021 to December 2021 was used for the analysis. The results suggest that the observed vertical noise levels are within the defined global noise limits for all stations. In the short-period band, the stations are dominated by cultural noise due to human activities, industrial machinery, automobile traffic, etc., which also causes a strong variation between daytime and night-time. At the long-period band, the vertical component lies above the new low-noise model (NLNM) and the horizontal components marginally overshoot the new high-noise model (NHNM) due to the tilt effect. An assessment of the noise variation was also performed during the partial lockdown due to COVID-19 in 2021 imposed by the Telangana state government in India, which revealed no significant variations in short-period noise levels. It may show the effect of remote installation away from cultural noise. Overall, our estimation suggests stability and validates the performance of all the installed stations of the Eastern Dharwar Craton in Telangana state. Research Highlights: In this study, we performed ambient noise analysis for ten broadband seismic stations of the Eastern Dharwar Craton, in Telangana state. We observed a significant diurnal variation as well as seasonal variation at all stations. We compared the noise variation during the period of partial lockdown in 2021 imposed by the Telangana state government in India and found no significant variations in short-period noise levels before and after partial lockdown. [ABSTRACT FROM AUTHOR]

Research within the language sciences has informed our understanding of how children build vocabulary knowledge especially during early childhood and the early school years. However, to date, our understanding of word learning in children is based primarily on research in quiet laboratory settings. The everyday environments that children inhabit such as schools, homes, and day cares are typically noisy. To better understand vocabulary development, we need to understand the effects of background noise on word learning. To gain this understanding, a cross-disciplinary approach between researchers in the language and hearing sciences in partnership with parents, educators, and clinicians is ideal. Through this approach we can identify characteristics of effective vocabulary instruction that take into account the background noise present in children's learning environments. Furthermore, we can identify characteristics of children who are likely to struggle with learning words in noisy environments. For example, differences in vocabulary knowledge, verbal working memory abilities, and attention skills will likely influence children's ability to learn words in the presence of background noise. These children require effective interventions to support their vocabulary development which subsequently should support their ability to process and learn language in noisy environments. Overall, this cross-disciplinary approach will inform theories of language development and inform educational and intervention practices designed to support children's vocabulary development. [ABSTRACT FROM AUTHOR]

Fractional vegetation cover (FVC) is a crucial indicator to estimate degradation and desertification for grasslands. However, traditional small-scale FVC analysis methods, such as visual estimation and point-sampling, are cumbersome and imprecise. Innovative methods like image-based FVC analysis methods, while accurate, face challenges such as complex analytical procedures and the necessary training for operations. Therefore, in this study, a combined application of ImageJ and Photoshop was employed to achieve a more effective analysis of FVC values in desertification areas. Our results showed that the FVC results obtained by combination of Photoshop and ImageJ were dependable and precise (R2 > 0.98), demonstrating equivalency to results obtained through either visual estimation or Photoshop-based methods. Furthermore, even in the face of background interference and varied shooting angles, the combination of ImageJ and Photoshop software was still able to maintain a low error rate when analyzing FVC values (average error rate = − 2.6%). In conclusion, the imaged-based combined FVC analysis method employed in our research was an effective, precise, and efficient technique for analyzing small-scale FVC, promising substantial improvement over conventional methods. [ABSTRACT FROM AUTHOR]

Underwater noise pollution has become a potential threat to aquatic animals in the natural environment. The main causes of such pollution are frequent human activities creating underwater environmental noise, including commercial shipping, offshore energy platforms, scientific exploration activities, etc. However, in aquaculture environments, underwater noise pollution has also become an unavoidable problem due to background noise created by aquaculture equipment. Some research has shown that certain fish show adaptability to noise over a period of time. This could be due to fish's special auditory organ, i.e., their "inner ear"; meanwhile, otoliths and sensory hair cells are the important components of the inner ear and are also essential for the function of the auditory system. Recently, research in respect of underwater noise pollution has mainly focused on adult fish, and there is a lack of the research on the effects of underwater noise pollution on the development process of the auditory system in the embryonic development period. Thus, in this study, we collected embryo–larval samples of the small yellow croaker (Larimichthys polyactis) in four important stages of otic vesicle development through artificial breeding. Then, we used metabonomics and transcriptomics analyses to reveal the development process of the auditory system in the embryonic development period under background noise (indoor and underwater environment sound). Finally, we identified 4026 differentially expressed genes (DEGs) and 672 differential metabolites (DMs), including 37 DEGs associated with the auditory system, and many differences mainly existed in the neurula stage (20 h of post-fertilization/20 HPF). We also inferred the regulatory mode and process of some important DEGs (Dnmt1, CPS1, and endothelin-1) in the early development of the auditory system. In conclusion, we suggest that the auditory system development of L. polyactis begins at least in the neurula stage or earlier; the other three stages (tail bud stage, caudal fin fold stage, and heart pulsation stage, 28–35 HPF) mark the rapid development period. We speculate that the effect of underwater noise pollution on the embryo–larval stage probably begins even earlier. [ABSTRACT FROM AUTHOR]

Speech-in-noise testing has been proposed as a useful part of the audiometric test battery dating back to the earliest years of the field of audiology. Many speech-in-noise tests have been developed and used to varying degrees. However, multiple barriers have prevented speech-in-noise testing from being used widely in the clinic. The purpose of this article is to provide a resource to audiologists and other hearing health professionals who want to know (1) what tests are available for use, (2) the rationale behind specific tests, and (3) important considerations when selecting one or more tests to use clinically. In addition, data are presented for four speech-in-noise tests with the purpose of comparing outcomes as a function of age and hearing status. The four tests (QuickSIN, Words in Noise [WIN], Listening in Spatialized Noise–Sentences [LiSN-S], and Coordinate Response Measure [CRM]) were completed by 30 individuals from three groups: 10 young adults with normal hearing, 10 older adults with normal hearing, and 10 older adults with hearing loss. The results suggest that, despite significant differences in performance between groups, group overlap was present such that some individuals from one group performed similar to some individuals of other groups; therefore, individual performance was more important than associated group. When selecting an appropriate speech-in-noise test to use clinically, audiologists should carefully consider the purpose of their testing and the type of information they desire as an outcome. A quick-resource table and appendix is provided to aid audiologists and other health professionals in their selection of an appropriate speech-in-noise test. [ABSTRACT FROM AUTHOR]

This paper examines speech privacy in both residential and commercial spaces. The ASTM E2638 standard defines the Speech Privacy Class (SPC) parameter, which measures speech privacy based on the signal-to-noise ratio at the listener's position. This paper proposes estimating the SPC value using relevant ISO parameters commonly used in European practice: the apparent sound reduction index in dB (defined by ISO 16283-1, 2 standards) and the equivalent ambient noise level in dBA (defined by the ISO 1996-1 standard). The estimated value of the SPC parameter in this paper is referred to as the Speech Privacy Index (SPI). A diverse range of situations, i.e., rooms, was analyzed in the field. These rooms varied in terms of purpose, organization, dimensions, furnishings, isolation from other spaces, and internal and external environments. The results of the experiments demonstrate a strong correlation between the SPC value estimated according to ISO parameters (the proposed method) and the SPC as defined in the ASTM E standard. This indicates that the proposed method can provide an indicator of the state of speech privacy in buildings. The significance of the proposed calculation method (i.e., the STI parameter) lies in its ability to be applied at the building design stage, as well as after its completion, during routine testing. [ABSTRACT FROM AUTHOR]

Due to its spread via physical contact and the regulations on wearing face masks, COVID-19 has resulted in tough challenges for speaker recognition. Masks may aid in preventing COVID-19 transmission, although the implications of the mask on system performance in a clean environment and with varying levels of background noise are unclear. The face mask has an impact on speech output. The task of comprehending speech while wearing a face mask is made more difficult by the mask's frequency response and radiation qualities, which is vary depending on the material and design of the mask. In this study, we recorded speech while wearing a face mask to see how different masks affected a state-of-the-art text-independent speaker verification system using an i-vector speaker identification system. This research investigates the influence of facial coverings on speaker verification. To address this, we investigated the effect of fabric masks on speaker identification in a cafeteria setting. These results present preliminary speaker recognition rates as well as mask verification trials. The result shows that masks had little to no effect in low background noise, with an EER of 2.4–2.5% in 20 dB SNR for both masks compared to no mask at the same level. In noisy conditions, accuracy was 12.7–13.0% lowers than without a mask with a 5 dB SNR, indicating that while different masks perform similarly in low background noise levels, they become more noticeable in high noise levels. [ABSTRACT FROM AUTHOR]

Based on the analysis of the observation records from the three devices used by the Jiangxi Earthquake Early Warning Network (seismometer, strong seismometer, and intensity meter), the RMS value and power spectrum of the station site background noise indicate that the seismometer can effectively record the background noise in a wide frequency domain, while the strong seismometer can only effectively record natural noise above 0.1 Hz. In contrast, the intensity meter only records the noise of the device itself, and cannot effectively record microtremor. However, all three kinds of instruments can effectively record seismic events, and can be used for seismic location through the identification of initial movement of P wave. However, when determining the magnitude, the results of strong seismometer and intensity meter will be significantly larger than those of seismometer, so they are only used for reference in practical application.

Noise-induced tinnitus is generally associated with hearing impairment caused by traumatic acoustic overexposure. Previous studies in laboratory animals and human subjects, however, have observed differences in tinnitus susceptibility, even among individuals with similar hearing loss. The mechanisms underlying increased sensitivity or, conversely, resistance to tinnitus are still incompletely understood. Here, we used behavioral tests and ABR audiometry to compare the sound-evoked responses of mice that differed in the presence of noise-induced tinnitus. The aim was to find a specific pre-exposure neurophysiological marker that would predict the development of tinnitus after acoustic trauma. Noiseexposed mice were screened for tinnitus-like behavior with the GPIAS paradigm and subsequently divided into tinnitus (+T) and non-tinnitus (−T) groups. Both groups showed hearing loss after exposure, manifested by elevated audiometric thresholds along with reduced amplitudes and prolonged latencies of ABR waves. Prior to exposure, except for a slightly increased slope of growth function for ABR amplitudes in +T mice, the two groups did not show significant audiometric differences. Behavioral measures, such as the magnitude of the acoustic startle response and its inhibition by gap pre-pulse, were also similar before exposure in both groups. However, +T mice showed significantly increased suppression of the acoustic startle response in the presence of background noise of moderate intensity. Thus, increased modulation of startle by background sounds may represent a behavioral correlate of susceptibility to noise-induced tinnitus, and its measurement may form the basis of a simple non-invasive method for predicting tinnitus development in laboratory rodents. [ABSTRACT FROM AUTHOR]

Successful and efficient pest management is key to sustainable horticultural food production. While greenhouses already allow digital monitoring and control of their climate parameters, a lack of digital pest sensors hinders the advent of digital pest management systems. To close the control loop, digital systems need to be enabled to directly assess the state of different insect populations in a greenhouse. The presented article investigates the feasibility of acoustic sensors for insect detection in greenhouses. The study is based on an extensive dataset of acoustic insect recordings made with an array of high-quality microphones under noise-shielded conditions. By mixing these noise-free laboratory recordings with environmental sounds recorded with the same equipment in a greenhouse, different signal-to-noise ratios (SNR) are simulated. To explore the possibilities of this unique and novel dataset, two deep-learning models are trained on this simulation data. A simple spectrogram-based model represents the baseline for a comparison with a model capable of processing multi-channel raw audio data. Making use of the unique possibility of the dataset, the models are pre-trained on clean data and fine-tuned on noisy data. Under lab conditions, results show that both models can make use of not just insect flight sounds but also the much quieter sounds of insect movements. First attempts under simulated real-world conditions showed the challenging nature of this task and the potential of spatial filtering. The analysis enabled by the proposed methods for training and evaluation provided valuable insights that should be considered for future work. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background. Animal communication consists of signal production and perception, which are crucial for social interactions. The main form used by anurans is auditory communication, in most cases produced as advertisement calls. Furthermore, sound perception happens mainly through an external tympanic membrane, and plays an important role in social behavior. In this study, we evaluated the influence of body and tympanic membrane sizes on call frequency across the phylogeny of anurans. Methods. We use data on snout-vent length, tympanic membrane diameter, and dominant frequency of the advertisement call from the literature and from natural history museum collections. We mapped these traits across the anuran phylogeny and tested different models of diversification. Our final dataset includes data on body size, tympanic membrane size, and call dominant frequency of 735 anuran species. Results. The best explanatory model includes body and tympanum size with no interaction term. Although our results show that call frequency is strongly constrained by body and tympanum size, we identify five evolutionary shifts in allometry from that ancestral constraint. We relate these evolutionary shifts to the background noise experienced by populations. Body size is important for myriad ecological interactions and tympanum size is strongly associated with female call frequency preferences. Thus, allometric escape in frog calls might arise through environmental selection such as breeding in fast flowing or soundscape competition, as well as sexual selection linked to tympanum size. [ABSTRACT FROM AUTHOR]

Speech perception is challenging under adverse conditions. However, there is limited evidence regarding how multiple adverse conditions affect speech perception. The present study investigated two conditions that are frequently encountered in real-life communication: background noise and breathy vocal quality. The study first examined the effects of background noise and breathiness on speech perception as measured by intelligibility. Secondly, the study tested the hypothesis that both noise and breathiness affect listening effort, as indicated by linear and nonlinear changes in pupil dilation. Low-context sentences were resynthesized to create three levels of breathiness (original, mild-moderate, and severe). The sentences were presented in a fluctuating nonspeech noise with two signal-to-noise ratios (SNRs) of −5 dB (favorable) and −9 dB (adverse) SNR. Speech intelligibility and pupil dilation data were collected from young listeners with normal hearing thresholds. The results demonstrated that a breathy vocal quality presented in noise negatively affected speech intelligibility, with the degree of breathiness playing a critical role. Listening effort, as measured by the magnitude of pupil dilation, showed significant effects with both severe and mild-moderate breathy voices that were independent of noise level. The findings contributed to the literature by demonstrating the impact of vocal quality on the perception of speech in noise. They also highlighted the complex dynamics between overall task demand and processing resources in understanding the combined impact of multiple adverse conditions. [ABSTRACT FROM AUTHOR]

The ability to discriminate between talkers based on their fundamental (F0) and formant frequencies can facilitate speech comprehension in multi-talker environments. To date, voice discrimination (VD) of children and adults has only been tested in quiet conditions. This study examines the effect of speech-shaped noise on the use of F0 only, formants only, and the combined F0 + formant cues for VD. A total of 24 adults (18–35 years) and 16 children (7–10 years) underwent VD threshold assessments in quiet and noisy environments with the tested cues. Thresholds were obtained using a three-interval, three-alternative, two-down, one-up adaptive procedure. The results demonstrated that noise negatively impacted the utilization of formants for VD. Consequently, F0 became the lead cue for VD for the adults in noisy environments, whereas the formants were the more accessible cue for VD in quiet environments. For children, however, both cues were poorly utilized in noisy environments. The finding that robust cues such as formants are not readily available for VD in noisy conditions has significant clinical implications. Specifically, the reliance on F0 in noisy environments highlights the difficulties that children encounter in multi-talker environments due to their poor F0 discrimination and emphasizes the importance of maintaining F0 cues in speech-processing strategies tailored for hearing devices. [ABSTRACT FROM AUTHOR]

Abstract Background In droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events. Results Here, we characterize this background noise exemplified by three scRNA-seq and two snRNA-seq replicates of mouse kidneys. For each experiment, cells from two mouse subspecies are pooled, allowing to identify cross-genotype contaminating molecules and thus profile background noise. Background noise is highly variable across replicates and cells, making up on average 3–35% of the total counts (UMIs) per cell and we find that noise levels are directly proportional to the specificity and detectability of marker genes. In search of the source of background noise, we find multiple lines of evidence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure. Conclusions Our findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it.

Noise-induced tinnitus is generally associated with hearing impairment caused by traumatic acoustic overexposure. Previous studies in laboratory animals and human subjects, however, have observed differences in tinnitus susceptibility, even among individuals with similar hearing loss. The mechanisms underlying increased sensitivity or, conversely, resistance to tinnitus are still incompletely understood. Here, we used behavioral tests and ABR audiometry to compare the sound-evoked responses of mice that differed in the presence of noise-induced tinnitus. The aim was to find a specific pre-exposure neurophysiological marker that would predict the development of tinnitus after acoustic trauma. Noise-exposed mice were screened for tinnitus-like behavior with the GPIAS paradigm and subsequently divided into tinnitus (+T) and non-tinnitus (−T) groups. Both groups showed hearing loss after exposure, manifested by elevated audiometric thresholds along with reduced amplitudes and prolonged latencies of ABR waves. Prior to exposure, except for a slightly increased slope of growth function for ABR amplitudes in +T mice, the two groups did not show significant audiometric differences. Behavioral measures, such as the magnitude of the acoustic startle response and its inhibition by gap pre-pulse, were also similar before exposure in both groups. However, +T mice showed significantly increased suppression of the acoustic startle response in the presence of background noise of moderate intensity. Thus, increased modulation of startle by background sounds may represent a behavioral correlate of susceptibility to noise-induced tinnitus, and its measurement may form the basis of a simple non-invasive method for predicting tinnitus development in laboratory rodents.

Background Animal communication consists of signal production and perception, which are crucial for social interactions. The main form used by anurans is auditory communication, in most cases produced as advertisement calls. Furthermore, sound perception happens mainly through an external tympanic membrane, and plays an important role in social behavior. In this study, we evaluated the influence of body and tympanic membrane sizes on call frequency across the phylogeny of anurans. Methods We use data on snout-vent length, tympanic membrane diameter, and dominant frequency of the advertisement call from the literature and from natural history museum collections. We mapped these traits across the anuran phylogeny and tested different models of diversification. Our final dataset includes data on body size, tympanic membrane size, and call dominant frequency of 735 anuran species. Results The best explanatory model includes body and tympanum size with no interaction term. Although our results show that call frequency is strongly constrained by body and tympanum size, we identify five evolutionary shifts in allometry from that ancestral constraint. We relate these evolutionary shifts to the background noise experienced by populations. Body size is important for myriad ecological interactions and tympanum size is strongly associated with female call frequency preferences. Thus, allometric escape in frog calls might arise through environmental selection such as breeding in fast flowing or soundscape competition, as well as sexual selection linked to tympanum size.

Background: Intelligibility measurement is influenced by the characteristics of a speaker, listener and contextual factors. This study addresses the clinical problem of measuring speech intelligibility in children with velopharyngeal insufficiency (VPI) in real‐world conditions. Aims: The purpose of the study was to investigate the effects of background noise on speech intelligibility in speakers with velopharyngeal insufficiency (VPI) compared to typical speech. The study further determined the contribution of nasalance and articulation accuracy in judgments of intelligibility. Methods & Procedures: Fifteen speakers diagnosed with VPI and their typical peers provided audio recordings of 20 sentences from the Hearing in Noise Test. Speech samples were presented over quiet and noise (+5 dB signal‐to‐noise ratio) conditions to 70 naïve listeners. Intelligibility scores from naïve listeners' orthographic transcriptions were obtained as the percentage of correctly identified words. Outcomes & Results: A repeated‐measures analysis of variance showed diagnosis of VPI (F(1, 28) = 13.44, p = 0.001, and presence of noise (F(1, 28) = 39.18, p < 0.001) significantly affected the intelligibility scores. There was no interaction between the diagnosis of VPI and noise (F(1, 28) = 0.06, p = 0.80). The multivariate regression analysis indicated that nasalance and articulation accuracy explain a significant amount of variance in the intelligibility scores of VPI speakers in quiet (F(2, 12) = 7.11, p < 0.05, R2 = 0.55, R2Adjusted = 0.47) and noise (F(2, 12) = 6.32, p < 0.05, R2 = 0.51, R2Adjusted = 0.43), but the significance mainly came from the effect of percentage of consonants correct (β = 0.97, t(12) = 2.90, p = 0.01). Percentage of consonants correct significantly increased the speech intelligibility in either with or without noise conditions. Conclusions & Implications: The current work suggests that background noise will significantly affect reductions in intelligibility in both groups; the effect is more prominent in VPI speech. It was also further noted that articulation accuracy significantly affected intelligibility in quiet and noise rather than nasalance scores. WHAT THIS PAPER ADDS: What is already known on the subject: Intelligibility measurement is influenced by the characteristics of a speaker, listener and contextual factors. Accordingly, it is essential to determine the degree to which speech assessments in the clinic can predict communication difficulties in the presence of background noise in real life. Background noise can adversely cause speech intelligibility degradation in individuals with speech disorders. What this study adds: The study examined the effects of background noise on speech intelligibility in speakers with velopharyngeal insufficiency (VPI) secondary to cleft palate compared to typical speech. The study results suggested that the presence of background noise will significantly affect reductions in intelligibility in both groups; however, the effect is more prominent in VPI speech. What are the clinical implications of this work?: We found out that the intelligibility of VPI speech is lower in the presence of background noise, and therefore, assessments of speech intelligibility in clinical settings should take this into account. To ensure effective communication in noisy environments, recommended strategies include selecting quiet locations, eliminating potential distractions and supplementing communication with nonverbal cues. It is important to recognize that the effectiveness of these strategies may vary depending on the individual and the specific communication context. [ABSTRACT FROM AUTHOR]

Copyright of Progress in Earthquake Sciences is the property of China Earthquake Administration, Institute of Geophysics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background noise elicits listening effort. What else is tinnitus if not an endogenous background noise? From such reasoning, we hypothesized the occurrence of increased listening effort in tinnitus patients during listening tasks. Such a hypothesis was tested by investigating some indices of listening effort through electroencephalographic and skin conductance, particularly parietal and frontal alpha and electrodermal activity (EDA). Furthermore, tinnitus distress questionnaires (THI and TQ12-I) were employed. Parietal alpha values were positively correlated to TQ12-I scores, and both were negatively correlated to EDA; Pre-stimulus frontal alpha correlated with the THI score in our pilot study; finally, results showed a general trend of increased frontal alpha activity in the tinnitus group in comparison to the control group. Parietal alpha during the listening to stimuli, positively correlated to the TQ12-I, appears to reflect a higher listening effort in tinnitus patients and the perception of tinnitus symptoms. The negative correlation between both listening effort (parietal alpha) and tinnitus symptoms perception (TQ12-I scores) with EDA levels could be explained by a less responsive sympathetic nervous system to prepare the body to expend increased energy during the "fight or flight" response, due to pauperization of energy from tinnitus perception. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Investigación e Innovación en Ciencias de la Salud (RIICS) is the property of Fundacion Universitaria Maria Cano and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Investigación e Innovación en Ingenierías is the property of Universidad Simon Bolivar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the strong background noise environment, the vibration impact caused by the local fault of the main bearing during the service of the wind turbine is easily disturbed. At the same time, due to the influence of the complex and variable vibration transmission path, it is often difficult to accurately identify the fault of the main bearing. To solve this problem, a main bearing fault diagnosis method based on the singular value decomposition packet (SVDP) Gini index diagram and the adaptive Richardson-Lucy deconvolution (ARLD) was proposed, to extract weak fault features in strong background noise environment. Firstly, the decomposition level of SVDP was set to process the original signal, and the Gini indexes of different level components were calculated to construct SVDP Gini index image. Then, the optimal component could be separated from original signal and the signal noise ratio could be improved. Further, the optimal morphological control parameter of deconvolution algorithm was automatically acquired by whale optimization algorithm, the deconvolution process was performed on the optimal component to restrain the noise interferences and enhance the impact features. Finally, the envelope demodulation analysis of deconvolutionsignal was carried out using Teager energy operator, and the fault location of main bearing could be judged according to the feature frequency spectral lines in the envelope spectrum. The effectiveness and robustness of the diagnosis method based on SVDP Gini index image and ARLD were respectively verified by simulation signals and wind power field measured data. The research results show that the fault diagnosis method based on SVDP Gini index diagram and ARLD can effectively extract the fault characteristic frequency and multiple frequency components of the main bearing of wind turbine, and then accurately judge the weak fault of the main bearing. This method can provide some reference for weak fault diagnosis of main bearing of wind turbine in practical engineering. [ABSTRACT FROM AUTHOR]

Background noise due to nonspecific binding of biomolecules on the assay substrates is one of the most common challenges that limits the sensitivity of microarray-based immunoassays. Background signal intensity usually increases when complex biological fluids are used because they have a combination of molecules and vesicles that can adsorb onto substrate surfaces. Blocking strategies coupled with surface chemistries can reduce such nonspecific binding and improve assay sensitivity. In this paper, we conducted a systematic optimization of blocking strategies on a variety of commonly used substrates for protein measurement in complex biofluids. Four blocking strategies (BSA, non-fat milk, PEG, and a protein-free solution) coupled with four surface chemistries (3-glycidoxypropyltrimethoxysilane (GPS), poly-L-lysine (PLL), aminoalkylsilane (AAS), and nitrocellulose (NC)) were studied for their effect on background, microspot, and net signal intensities. We have also explored the effect that these blocking strategies have when proteins in complex samples (plasma, serum, cell culture media, and EV lysate) are measured. Irregular spot morphology could affect signal extraction using automated software. We found that the microspots with the best morphology were the ones printed on GPS glass surfaces for all immunoassays. On NC membrane, the protein-based blocking strategies yielded the highest net fluorescent intensity with the antigen contained in PBS, plasma, serum, and serum-free cell culture media. Differently, with EV lysate samples, Pierce™ protein-free blocker yielded the best net signal intensity on both GPS and NC surfaces. The choice of blocking strategies highly depends on the substrate. Moreover, the findings discovered in this study are not limited to microarray-based immunoassays but can provide insights for other assay formats. [ABSTRACT FROM AUTHOR]

In response to the challenges of karst geophysical exploration in an environment with strong external interference, this paper proposes a new method, namely the frequency-Bessel transform method, for extracting multi-order dispersion curves of surface waves from background noise to characterize karst. The observation noise data of the Wuhan karst development area are used as an example, where the dolomitic limestone and limestone mixed with dolomite of the Jialing River Formation of the middle lower Triassic are widely developed in the observation area. The frequency-Bessel transform method involves performing a Bessel integral transformation on the cross-correlation coefficient of background noise in the frequency domain. Firstly, by synthesizing theoretical noise data and comparing it with the spatial autocorrelation method—which is currently the main method for extracting the fundamental dispersion curve of surface waves—it is verified that the frequency-Bessel transform method can extract the higher-mode dispersion curve. Then, by taking the actual measured single-point noise data as an example, the effect of applying the frequency-Bessel transform to the actual noise data is tested, and the inversion of the fine structure of the strata by the addition of higher-mode dispersion, the use of the damped least squares inversion method, and the joint inversion of fundamental and higher-mode dispersion curves are analyzed. The higher-mode dispersion curve of Rayleigh surface wave extracted by the frequency-Bessel transform is much clearer, and the 2D shear wave velocity structure profile obtained from inversion explains the karst development area, karst strip area, and thickness of the Quaternary overburden. The inferred results match with the actual borehole data. Multi-mode imaging of background noise based on the frequency-Bessel method can be applied to depict karst in complex backgrounds, and has significant potentiality in the field of ambient seismic noise tomography, providing a new idea and method for karst detection in near-surface engineering. [ABSTRACT FROM AUTHOR]