Hypoactivation of the central auditory system in listeners who are hypertolerant of background noise.

Listeners exhibit varying levels of tolerance for background noise during speech communication. It has been proposed that low tolerance of background noise may be the consequence of abnormally amplified gain in the central auditory system (CAS). Here, using a dataset of young adults with normal hearing thresholds, we asked whether central gain mechanisms might also explain cases of hypertolerance of background noise, as well as cases of reduced, but not abnormal, tolerance. We used the auditory brainstem response to derive a measure of CAS gain (wave V/wave I ratio) to compare listeners' background noise tolerance while listening to speech, grouping them into three categories: hyper, high, and medium tolerance. We found that hypertolerant listeners had reduced CAS gain compared to those with high tolerance. This effect was driven by wave V not wave I. In addition, the medium tolerant listeners trended toward having reduced wave I and reduced wave V amplitudes and generally higher levels of exposure to loud sound, suggestive of the early stages of noise-compromised peripheral function without an apparent compensatory increase in central gain. Our results provide physiological evidence that 1) reduced CAS gain may account for hypertolerance of background noise but that 2) increased CAS gain is not a prerequisite for medium tolerance of background noise.NEW & NOTEWORTHY Our findings strengthen the proposed mechanistic connection between background noise tolerance and auditory physiology by suggesting a link between hypertolerance and reduced central auditory gain, measured by the auditory brainstem response.

Measuring the effect of adaptive directionality and split processing on noise acceptance at multiple input levels.

OBJECTIVE: This study used the multi-level Tracking of Noise Tolerance (TNT) test to compare Augmented Focus (AF) or split processing over non-AF processing with adaptive directionality (non-AF-dirm) and with an omnidirectional microphone (non-AF-omni). DESIGN: This was a single-blind, within-subject repeated measures design. STUDY SAMPLE: Nineteen listeners with a mild-to-moderate hearing loss. The listeners' task was to determine their tolerable noise level (TNL) in the sound-field at four fixed speech levels (i.e. 62, 68, 75, and 85 dB SPL) in the unaided condition and the different aided conditions. The speech passages were presented from 0° while a continuous speech-shaped noise was presented from 180°. Each condition was tested twice, each in a different counterbalanced order. RESULTS: AF improved TNL by an average of 2.9 dB over non-AF-dirm. Adaptive directionality improved the aided TNL by 4.7 dB over the non-AF-omni condition. The unaided TNL was similar to the aided non-AF-omni TNL. Whereas a stable TNL was reached in 20-30 s for non-AF-dirm, it took AF < 15 s to reach a stable TNL at all input levels. CONCLUSIONS: This study shows that AF allowed 2.9 dB of signal-to-noise ratio (SNR) improvement over that of non-AF-dirm and 7.6 dB over the aided non-AF-omni condition.

Performance of older normal-hearing listeners on the tracking of noise tolerance (TNT) test.

OBJECTIVE: To gather preliminary reference data on older normal-hearing (NH) adults for the refined Tracking of Noise Tolerance (TNT) test. DESIGN: Within-subject repeated measures. Participants were tested on the TNT in the sound-field and under headphones. In the sound-field, speech stimuli were presented at 75 dB SPL and 82 dB SPL from 0° with a speech-shaped noise presented either from 0° or 180° at a level controlled by the participants. The order of signal level, mode of presentation, noise azimuth, and TNT passages were counterbalanced across listeners. Testing was repeated for one condition after 1-3 weeks to estimate within-session and between-session reliability. STUDY SAMPLE: Twenty-five NH listeners (51-82 yrs of age). RESULTS: Mean TNT scores (TNTAve) were about 4 dB at a speech input of 75 dB SPL and 3 dB at 82 dB SPL. The TNTAve was similar between the headphone and sound-field presentations in the co-located noise. TNTAve scores measured with noise-back were about 1 dB better than those measured from the front. The 95% confidence intervals of absolute test-retest differences were about 1.2 dB within-session and 2.0 dB between sessions. CONCLUSIONS: The refined TNT may be a reliable tool to measure noise acceptance and subjective speech intelligibility.

Counterfactual Anonymous Quantum Teleportation in the Presence of Adversarial Attacks and Channel Noise.

Hiding the identity of involved participants in the network, known as anonymity, is a crucial issue in some cryptographic applications such as electronic voting systems, auctions, digital signatures, and Byzantine agreements. This paper proposes a new anonymous quantum teleportation protocol based on counterfactual communication where no information-carrying particles pass through the channel. It is achieved by the distribution of a counterfactual entanglement among the participants in the network followed by the establishment of an anonymous entanglement between the sender and the receiver. Afterwards, the sender can anonymously teleport a quantum state to the receiver by utilizing the anonymous entanglement. However, the practicality of the anonymous quantum network mainly calls for two performance measures-robustness against adversarial attacks and noisy environments. Motivated by these demands, firstly, we prove the security of our proposed protocol and show that it achieves both the sender and receiver's anonymity in the presence of active adversaries and untrusted parties. Along with anonymity, we also ensure the correctness of the protocol and the privacy of the teleported qubit. Finally, we analyze the robustness of our proposed protocol under the presence of channel noise and compare its fidelity with those of the conventional protocols. The main advantage of our proposed protocol is that it can provide useful anonymous quantum resources for teleportation under noisy environment with a higher security compared to previous protocols.

A Noise Tolerant Spread Spectrum Sound-Based Local Positioning System for Operating a Quadcopter in a Greenhouse.

Quadcopters are beginning to play an important role in precision agriculture. In order to localize and operate the quadcopter automatically in complex agricultural settings, such as a greenhouse, a robust positioning system is needed. In previous research, we developed a spread spectrum sound-based local positioning system (SSSLPS) with a 20 mm accuracy within a 30 × 30 m greenhouse area. In this research, a noise tolerant SSSLPS was developed and evaluated. First, the acoustic noise spectrum emitted by the quadcopter was documented, and then the noise tolerance properties of SSSounds were examined and tested. This was done in a greenhouse with a fixed quadcopter (9.75 N thrust) with the positioning system mounted on it. The recorded quadcopter noise had a broadband noise compared to the SSSound. Taking these SSSound properties into account, the noise tolerance of the SSSLPS was improved, achieving a positioning accuracy of 23.2 mm and 31.6 mm accuracy within 12 × 6 m for both Time-division Multiple Access (TDMA) and Frequency-division Multiple Access (FDMA) modulation. The results demonstrate that the SSSLPS is an accurate, robust positioning system that is noise tolerant and can used for quadcopter operation even within a small greenhouse.

A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

Constraints on Acceleration in Bilingual Development: Evidence from Word Segmentation by Spanish Learning Infants.

We have previously shown that bilingual Spanish and English-learning infants can segment English iambs, two-syllable words with final stress (e.g., guiTAR), earlier than their monolingual peers. This is consistent with accelerated development in bilinguals and was attributed to bilingual infants' increased exposure to iambs through Spanish; about 10% of English content words start with an unstressed syllable, compared to 40% in Spanish. Here, we evaluated whether increased exposure to a stress pattern alone is sufficient to account for acceleration in bilingual infants. In English, 90% of content words start with a stressed syllable (e.g., KINGdom), compared to 60% in Spanish. However, we found no evidence for accelerated segmentation of Spanish trochees by Spanish-English bilingual infants compared to their monolingual Spanish-learning peers. Based on this finding, we argue that merely increased exposure to a linguistic feature in one language does not result in accelerated development in the other. Instead, only the acquisition of infrequent patterns in one language may be accelerated due to the additive effects of the other language.

Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography.

Objective.Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the periphery of a heterogeneous conductor are inverted to map its internal conductivity. The EIT method proposed here aims to improve computational speed and noise tolerance by introducing sensitivity volume as a figure-of-merit for comparing EIT measurement protocols.Approach.Each measurement is shown to correspond to a sensitivity vector in model space, such that the set of measurements, in turn, corresponds to a set of vectors that subtend a sensitivity volume in model space. A maximal sensitivity volume identifies the measurement protocol with the greatest sensitivity and greatest mutual orthogonality. A distinguishability criterion is generalized to quantify the increased noise tolerance of high sensitivity measurements.Main result.The sensitivity volume method allows the model space dimension to be minimized to match that of the data space, and the data importance to be increased within an expanded space of measurements defined by an increased number of contacts.Significance.The reduction in model space dimension is shown to increasecomputational efficiency, accelerating tomographic inversion by several orders of magnitude, while the enhanced sensitivitytolerates higher noiselevels up to several orders of magnitude larger than standard methods.

Analysis on the inherent noise tolerance of feedforward network and one noise-resilient structure.

In the past few decades, feedforward neural networks have gained much attraction in their hardware implementations. However, when we realize a neural network in analog circuits, the circuit-based model is sensitive to hardware nonidealities. The nonidealities, such as random offset voltage drifts and thermal noise, may lead to variation in hidden neurons and further affect neural behaviors. This paper considers that time-varying noise exists at the input of hidden neurons, with zero-mean Gaussian distribution. First, we derive lower and upper bounds on the mean square error loss to estimate the inherent noise tolerance of a noise-free trained feedforward network. Then, the lower bound is extended for any non-Gaussian noise cases based on the Gaussian mixture model concept. The upper bound is generalized for any non-zero-mean noise case. As the noise could degrade the neural performance, a new network architecture is designed to suppress the noise effect. This noise-resilient design does not require any training process. We also discuss its limitation and give a closed-form expression to describe the noise tolerance when the limitation is exceeded.

Biological convolutions improve DNN robustness to noise and generalisation.

Deep Convolutional Neural Networks (DNNs) have achieved superhuman accuracy on standard image classification benchmarks. Their success has reignited significant interest in their use as models of the primate visual system, bolstered by claims of their architectural and representational similarities. However, closer scrutiny of these models suggests that they rely on various forms of shortcut learning to achieve their impressive performance, such as using texture rather than shape information. Such superficial solutions to image recognition have been shown to make DNNs brittle in the face of more challenging tests such as noise-perturbed or out-of-distribution images, casting doubt on their similarity to their biological counterparts. In the present work, we demonstrate that adding fixed biological filter banks, in particular banks of Gabor filters, helps to constrain the networks to avoid reliance on shortcuts, making them develop more structured internal representations and more tolerance to noise. Importantly, they also gained around 20-35% improved accuracy when generalising to our novel out-of-distribution test image sets over standard end-to-end trained architectures. We take these findings to suggest that these properties of the primate visual system should be incorporated into DNNs to make them more able to cope with real-world vision and better capture some of the more impressive aspects of human visual perception such as generalisation.

Noninvasive estimation of aortic pressure waveform based on simplified Kalman filter and dual peripheral artery pressure waveforms.

BACKGROUND AND OBJECTIVE: Aortic pressure (Pa) is important for the diagnosis of cardiovascular disease. However, its direct measurement is invasive, not risk-free, and relatively costly. In this paper, a new simplified Kalman filter (SKF) algorithm is employed for the reconstruction of the Pa waveform using dual peripheral artery pressure waveforms. METHODS: Pa waveforms obtained in a previous study were collected from 25 patients. Simultaneously, radial and femoral pressure waveforms were generated from two simulation experiments, using transfer functions. In the first, the transfer function is a known finite impulse response; and in the second, it is derived from a tube-load model. To analyze the performance of the proposed SKF algorithm, variable amounts of noise were added to the observed output signal, to give a range of signal-to-noise ratios (SNRs). Additionally, central aortic, brachial and femoral pressure waveforms were simultaneously collected from 2 Sprague-Dawley rats and the measured and reconstructed Pa waveforms were compared. RESULTS: The proposed SKF algorithm outperforms canonical correlation analysis (CCA), which is the current state-of-the-art blind system identification method for the non-invasive estimation of central aortic blood pressure. It is also shown that the proposed SKF algorithm is more noise-tolerant than the CCA algorithm over a wide range of SNRs. CONCLUSION: The simulations and animal experiments illustrate that the proposed SKF algorithm is accurate and stable in the face of low SNRs. Improved methods for estimating central blood pressure as a measure of cardiac load adds to their value as a prognostic and diagnostic tool.

Personality captures dissociations of subjective versus objective hearing in noise.

Acoustic noise is pervasive in human environments. Some individuals are more tolerant to noise than others. We demonstrate the explanatory potential of Big-5 personality traits neuroticism (being emotionally unstable) and extraversion (being enthusiastic, outgoing) on subjective self-report and objective psycho-acoustic metrics of hearing in noise in two samples (total N = 1103). Under statistical control for demographics and in agreement with pre-registered hypotheses, lower neuroticism and higher extraversion independently explained superior self-reported noise resistance, speech-hearing ability and acceptable background noise levels. Surprisingly, objective speech-in-noise recognition instead increased with higher levels of neuroticism. In turn, the bias in subjectively overrating one's own hearing in noise decreases with higher neuroticism but increases with higher extraversion. Of benefit to currently underspecified frameworks of hearing in noise and tailored audiological treatments, these results show that personality explains inter-individual differences in coping with acoustic noise, which is a ubiquitous source of distraction and a health hazard.

Aortic pressure waveform reconstruction using a multi-channel Newton blind system identification algorithm.

BACKGROUND: Central aortic pressure (CAP) as the major load on the left heart is of great importance in the diagnosis of cardiovascular disease. Studies have pointed out that CAP has a higher predictive value for cardiovascular disease than peripheral artery pressure (PAP) measured by means of traditional sphygmomanometry. However, direct measurement of the CAP waveform is invasive and expensive, so there remains a need for a reliable and well validated non-invasive approach. METHODS: In this study, a multi-channel Newton (MCN) blind system identification algorithm was employed to noninvasively reconstruct the CAP waveform from two PAP waveforms. In simulation experiments, CAP waveforms were recorded in a previous study, on 25 patients and the PAP waveforms (radial and femoral artery pressure) were generated by FIR models. To analyse the noise-tolerance of the MCN method, variable amounts of noise were added to the peripheral signals, to give a range of signal-to-noise ratios. In animal experiments, central aortic, brachial and femoral pressure waveforms were simultaneously recorded from 2 Sprague-Dawley rats. The performance of the proposed MCN algorithm was compared with the previously reported cross-relation and canonical correlation analysis methods. RESULTS: The results showed that the root mean square error of the measured and reconstructed CAP waveforms and less noise-sensitive using the MCN algorithm was smaller than those of the cross-relation and canonical correlation analysis approaches. CONCLUSION: The MCN method can be exploited to reconstruct the CAP waveform. Reliable estimation of the CAP waveform from non-invasive measurements may aid in early diagnosis of cardiovascular disease.

A new noise-tolerant and predefined-time ZNN model for time-dependent matrix inversion.

In this work, a new zeroing neural network (ZNN) using a versatile activation function (VAF) is presented and introduced for solving time-dependent matrix inversion. Unlike existing ZNN models, the proposed ZNN model not only converges to zero within a predefined finite time but also tolerates several noises in solving the time-dependent matrix inversion, and thus called new noise-tolerant ZNN (NNTZNN) model. In addition, the convergence and robustness of this model are mathematically analyzed in detail. Two comparative numerical simulations with different dimensions are used to test the efficiency and superiority of the NNTZNN model to the previous ZNN models using other activation functions. In addition, two practical application examples (i.e., a mobile manipulator and a real Kinova JACO2 robot manipulator) are presented to validate the applicability and physical feasibility of the NNTZNN model in a noisy environment. Both simulative and experimental results demonstrate the effectiveness and tolerant-noise ability of the NNTZNN model.

Effect of noise tolerance on non-restorative sleep: a population-based study in Hong Kong.

OBJECTIVE: The aim of this study was to assess the impact of auditory noise tolerance on non-restorative sleep using an objective audiometric test in a representative sample. DESIGN: A total of 202 Chinese individuals aged 15 years and above were recruited from a population-based household survey. Their non-restorative sleep was assessed by a single item, the degree of feeling refreshed on waking up, on a 0-10 scale, while noise tolerance was measured by the most comfortable level expressed in A-weighted decibels. RESULTS: The 202 individuals (106 women) had a mean degree of feeling refreshed on waking up of 6.5 on the 0-10 scale and a mean maximum comfortable sound level of 69.2 dB. A multivariable analysis showed that a 1 dB increase in noise tolerance was associated with a 0.1-unit increase in the degree of feeling refreshed after adjusting for age, education, marital status, occupation, exercise, smoking, alcohol consumption, household noise level, stress, anxiety and depression. Moreover, housewives, non-smokers and individuals who were less anxious or stressed felt significantly more refreshed on waking up. CONCLUSION: People with higher levels of noise tolerance experienced more refreshing sleep. Additional clinical consideration of enhancing noise tolerance in patients with sleep complaints is needed.

Capacity, Fidelity, and Noise Tolerance of Associative Spatial-Temporal Memories Based on Memristive Neuromorphic Networks.

We have calculated key characteristics of associative (content-addressable) spatial-temporal memories based on neuromorphic networks with restricted connectivity-"CrossNets." Such networks may be naturally implemented in nanoelectronic hardware using hybrid memristive circuits, which may feature extremely high energy efficiency, approaching that of biological cortical circuits, at much higher operation speed. Our numerical simulations, in some cases confirmed by analytical calculations, show that the characteristics depend substantially on the method of information recording into the memory. Of the four methods we have explored, two methods look especially promising-one based on the quadratic programming, and the other one being a specific discrete version of the gradient descent. The latter method provides a slightly lower memory capacity (at the same fidelity) then the former one, but it allows local recording, which may be more readily implemented in nanoelectronic hardware. Most importantly, at the synchronous retrieval, both methods provide a capacity higher than that of the well-known Ternary Content-Addressable Memories with the same number of nonvolatile memory cells (e.g., memristors), though the input noise immunity of the CrossNet memories is lower.

Effects of multitasking on operator performance using computational and auditory tasks.

This study investigated the effects of multiple cognitive tasks on human performance. Twenty-four students at North Carolina A&T State University participated in the study. The primary task was auditory signal change perception and the secondary task was a computational task. Results showed that participants' performance in a single task was statistically significantly different from their performance in combined tasks: (a) algebra problems (algebra problem primary and auditory perception secondary); (b) auditory perception tasks (auditory perception primary and algebra problems secondary); and (c) mean false-alarm score in auditory perception (auditory detection primary and algebra problems secondary). Using signal detection theory (SDT), participants' performance measured in terms of sensitivity was calculated as -0.54 for combined tasks (algebra problems the primary task) and -0.53 auditory perceptions the primary task. During auditory perception tasks alone, SDT was found to be 2.51. Performance was 83% in a single task compared to 17% when combined tasks.

Noise tolerance in a Neocognitron-like network.

The Neocognitron and its related hierarchical models have been shown to be competitive in recognizing handwritten digits and objects. However, the tolerance of these models to several types of noise can be low. We will start by briefly overviewing some previous results regarding the tolerance of these models. Afterwards, we report the higher noise tolerance of the winner-take-all response in a hierarchical model over related models. We provide an analysis and interpretation of this tolerance under Bayesian decision theory. Finally, we report on how to further improve recognition for extremely noisy patterns.