Noisy Environment Research Articles

Ground-state cooling of a mechanical oscillator by a noisy environment

Dissipation and the accompanying fluctuations are often seen as detrimental for quantum systems since they are associated with fast relaxation and loss of phase coherence. However, it has been proposed that a pure state can be prepared if external noise induces suitable downwards transitions, while exciting transitions are blocked. We demonstrate such a refrigeration mechanism in a cavity optomechanical system, where we prepare a mechanical oscillator in its ground state by injecting strong electromagnetic noise at frequencies around the red mechanical sideband of the cavity. The optimum cooling is reached with a noise bandwidth smaller than but on the order of the cavity decay rate. At higher bandwidths, cooling is less efficient as suitable transitions are not effectively activated. In the opposite regime where the noise bandwidth becomes comparable to the mechanical damping rate, damping follows the noise amplitude adiabatically, and the cooling is also suppressed.

MelTrans: Mel-Spectrogram Relationship-Learning for Speech Emotion Recognition via Transformers.

Speech emotion recognition (SER) is not only a ubiquitous aspect of everyday communication, but also a central focus in the field of human-computer interaction. However, SER faces several challenges, including difficulties in detecting subtle emotional nuances and the complicated task of recognizing speech emotions in noisy environments. To effectively address these challenges, we introduce a Transformer-based model called MelTrans, which is designed to distill critical clues from speech data by learning core features and long-range dependencies. At the heart of our approach is a dual-stream framework. Using the Transformer architecture as its foundation, MelTrans deciphers broad dependencies within speech mel-spectrograms, facilitating a nuanced understanding of emotional cues embedded in speech signals. Comprehensive experimental evaluations on the EmoDB (92.52%) and IEMOCAP (76.54%) datasets demonstrate the effectiveness of MelTrans. These results highlight MelTrans's ability to capture critical cues and long-range dependencies in speech data, setting a new benchmark within the context of these specific datasets. These results highlight the effectiveness of the proposed model in addressing the complex challenges posed by SER tasks.

Clique-like Point Cloud Registration: A Flexible Sampling Registration Method Based on Clique-like for Low-Overlapping Point Cloud.

Three-dimensional point cloud registration is a critical task in 3D perception for sensors that aims to determine the optimal alignment between two point clouds by finding the best transformation. Existing methods like RANSAC and its variants often face challenges, such as sensitivity to low overlap rates, high computational costs, and susceptibility to outliers, leading to inaccurate results, especially in complex or noisy environments. In this paper, we introduce a novel 3D registration method, CL-PCR, inspired by the concept of maximal cliques and built upon the SC2-PCR framework. Our approach allows for the flexible use of smaller sampling subsets to extract more local consensus information, thereby generating accurate pose hypotheses even in scenarios with low overlap between point clouds. This method enhances robustness against low overlap and reduces the influence of outliers, addressing the limitations of traditional techniques. First, we construct a graph matrix to represent the compatibility relationships among the initial correspondences. Next, we build clique-likes subsets of various sizes within the graph matrix, each representing a consensus set. Then, we compute the transformation hypotheses for the subsets using the SVD algorithm and select the best hypothesis for registration based on evaluation metrics. Extensive experiments demonstrate the effectiveness of CL-PCR. In comparison experiments on the 3DMatch/3DLoMatch datasets using both FPFH and FCGF descriptors, our Fast-CL-PCRv1 outperforms state-of-the-art algorithms, achieving superior registration performance. Additionally, we validate the practicality and robustness of our method with real-world data.

Collective noise-resistant multi-party semi-quantum secret sharing protocols

Semi-quantum secret sharing facilitates the sharing of private data between quantum users and ‘classical’ users with limited quantum capabilities, thereby lowering the barrier to utilizing quantum technology. However, most current semi-quantum secret sharing protocols are confined to ideal environments and two-party scenarios. In this paper, we design two collective noise-resistant multi-party semi-quantum secret sharing protocols based on decoherence-free states to address potential noise interference during transmission. These protocols use decoherence-free states as information carriers for data interaction and exhibit strong resilience to both internal and external threats. We also conduct simulation experiments using IBM Qiskit to verify the stability and feasibility of the protocols in the noisy environments. The results of these experiments underscore the robustness of the protocols, particularly in the presence of collective noise. Compared with previous related protocols, our protocols have advantages in noise resistance and applicability to multi-party scenarios. Therefore, the proposed protocols may be more in line with the secret sharing needs of actual environments.

Cross-domain manifold structure preservation for transferable and cross-machine fault diagnosis

To address the decline or failure in the autonomous learning capability of traditional transfer learning methods when training and test samples come from different machines, resulting in low cross-machine fault diagnosis rates, we propose a cross-domain manifold structure preservation (CDMSP) method for diagnosing rolling bearing faults across machines. The CDMSP method can induce the manifold space projection matrices of the source and target domains more effectively. This method maps high-dimensional features into a low-dimensional manifold, preserving non-linear relationships and aligning distribution differences while maintaining cross-domain manifold structure consistency. Additionally, highly confidently labeled target domain samples are selected from each mapping result and added to the training dataset to enhance subspace learning in subsequent iterations. The CDMSP method is both simple and effective at capturing the underlying structures and patterns in the data. The CWRU dataset and our self-built test platform dataset were used to validate this method. Experimental results show that CDMSP, as a non-deep domain adaptation method of transfer learning, outperforms similar methods in cross-machine fault identification, achieving a maximum fault identification accuracy of 100 % with excellent convergence performance. Furthermore, simulated diagnostic experiments under noise interference indicate that CDMSP maintains high fault identification accuracy, even in noisy environments. Overall, CDMSP is an efficient and reliable new method for diagnosing cross-machine bearing faults.

StethoSpeech: Speech Generation Through a Clinical Stethoscope Attached to the Skin

We introduce StethoSpeech, a silent speech interface that transforms flesh-conducted vibrations behind the ear into speech. This innovation is designed to improve social interactions for those with voice disorders, and furthermore enable discreet public communication. Unlike prior efforts, StethoSpeech does not require (a) paired-speech data for recorded vibrations and (b) a specialized device for recording vibrations, as it can work with an off-the-shelf clinical stethoscope. The novelty of our framework lies in the overall design, simulation of the ground-truth speech, and a sequence-to-sequence translation network, which works in the latent space. We present comprehensive experiments on the existing CSTR NAM TIMIT Plus corpus and our proposed StethoText: a large-scale synchronized database of non-audible murmur and text for speech research. Our results show that StethoSpeech provides natural-sounding and intelligible speech, significantly outperforming existing methods on several quantitative and qualitative metrics. Additionally, we showcase its capacity to extend its application to speakers not encountered during training and its effectiveness in challenging, noisy environments. Speech samples are available at https://stethospeech.github.io/StethoSpeech/.

Formation control of high-order linear discrete-time multiple unmanned aerial vehicles systems in noisy environments

Formation control of high-order linear discrete-time multiple unmanned aerial vehicles (multi-UAVs) systems with multiplicative noises is studied in this article. Because of the existence of multiplicative noises, classical methods for discrete-time system control problems should be improved. Here, we develop a new discrete-time formation protocol for multi-UAVs systems. The stochastic Lyapunov stability theory and the positive definiteness theory of a generalized algebraic Riccati equation are then used to derive sufficient conditions for formation feasibility.

Shuffle-fusion pyramid network for bearing fault diagnosis under noisy environments

Abstract Recent advancements in deep learning have propelled the exploration of big data-driven fault diagnosis techniques. Nevertheless, traditional models often suffer from prohibitive computational demands, rendering them impractical for on-site deployment in rolling bearing fault diagnosis. To address this challenge, this paper introduces a novel lightweight fault diagnosis model with the pyramid architecture, named Shuffle-Fusion Pyramid Network (Shuffle-FPN). The model heralds several innovations: (1) A pyramid structure is designed to amalgamate fault signals across various scales, enlarging the network’s breadth and curtailing its depth. (2) Depth wise separable convolutions are adopted to streamline network parameters, thus achieving a lightweight model, and channel shuffling to ensure thorough information fusion across convolutional channels. (3) A global representation module is employed to offset the loss of global context, which accompanies increased convolutional depth. Collectively, these innovations enable Shuffle-FPN to extract nuanced fault features amidst noise and to operate on devices with limited memory, ensuring real-time fault diagnosis even in complex environments. Rigorous experiments on public datasets from Paderborn University (PU), supplemented by validations with our research group’s experiment data, reveal that Shuffle-FPN excels in fault identification under severe noise conditions and reduces memory footprint, which solidifies the superiority of Shuffle-FPN for practical fault diagnosis.

Stochastic effects on plankton dynamics: Insights from a realistic 0-dimensional marine biogeochemical model

Marine ecosystems exist in a noisy and uncertain environment, not governed by deterministic laws. The development of ecological communities is significantly influenced by variability, and the interaction between nonlinearity and stochastic processes can lead to phenomena that deterministic models cannot explain. Plankton, forming the base of the marine food web, are highly affected by stochastic fluctuations due to their short reproductive timescales. Investigating the effects of noise on plankton growth is essential for accurately describing and predicting marine health. We present a realistic biogeochemical model where multiplicative white noise represents environmental stochasticity affecting plankton. The model suggests ergodic properties in the presence of stochastic fluctuations, with temporal and ensemble distributions being coherent. Analytical and numerical analyses reveal that, given sufficiently low noise intensity, dynamics near equilibrium resemble an Ornstein-Uhlenbeck additive process. With higher noise intensities, resonance occurs, particularly when endogenous dynamics are periodic. The results indicate that low noise intensity can positively influence plankton persistence with an higher number of species coexisting, while higher noise intensity can establish a new equilibrium in the system.

Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning

Structural damage detection is crucial for maintaining the health and safety of buildings. However, achieving high accuracy in damage detection remains challenging, especially in noisy environments. To improve the accuracy and noise robustness of damage detection, this study proposes a novel method that combines the Conformer model and the dual-channel pseudo-supervised (DCPS) learning strategy for structural damage detection. The DCPS learning strategy improves the stability and accuracy of the model in noisy environments. It enables the model to input acceleration signals with different noise levels into each branch of the dual-channel network, thereby learning noise-robust features. The Conformer model, as the backbone network, integrates the advantages of convolutional neural networks (CNNs) and Transformers to effectively extract both local and global features from acceleration signals. The proposed method is validated using a four-story single-span steel-frame building model and the IASC-ASCE simulated benchmark structure. The results show that the proposed method achieves a higher classification accuracy than existing structural damage detection methods. Compared to the single Conformer-based method, this method improves the accuracy by 1.57% and 4.93% for the two validation structures, respectively. Moreover, the proposed method benefits from the DCPS learning strategy’s ability to achieve superior noise robustness compared to other methods. The proposed method holds potential value for improving the accuracy of damage detection and noise robustness in scenarios such as maintenance and extreme events.

A verified training support vector machine in bearing fault diagnosis

Abstract The combination of support vector machine (SVM) and deep learning is widely used in bearing fault diagnosis. The SVM-based bearing fault diagnosis method usually uses features extracted from bearing vibration signals as input. However, environmental noise in industrial applications can affect the feature representation, potentially leading to failures in SVM-based fault diagnosis methods. Adversarial robustness verification methods can evaluate the robustness of models to small perturbations in feature representations. Certified defense methods achieve robustness enhancement by embedding adversarial robustness verification into the learning process of the model. Thus, we propose a certified defense method named verified training SVM (VT-SVM) based on Lagrange duality. The proposed method incorporates adversarial robustness verification into the SVM learning framework, thereby enhancing the robustness of SVM in noisy environments. First, the certified defense optimization problem of SVM is constructed by combining the original optimization problem of SVM with the adversarial robustness verification problem. Then, the certified defense optimization problem is converted into an equivalent convex quadratic programming problem by introducing slack variables. Next, the Lagrange duality is used to transform the convex quadratic programming problem into a dual problem. Finally, the solution to the original optimization problem can be obtained by solving the dual problem. We validate the effectiveness of VT-SVM on the artificial dataset, the rolling bearing dataset from Case Western Reserve University, and the VBL-VA001 dataset from Sepuluh Nopember Institute of Technology. Experimental results demonstrate that our method both ensures high predictive performance and improves the resistance of the model to small noise.

No Evidence of Musical Training Influencing the Cortical Contribution to the Speech-Frequency-Following Response and Its Modulation through Selective Attention.

Musicians can have better abilities to understand speech in adverse condition such as background noise than non-musicians. However, the neural mechanisms behind such enhanced behavioral performances remain largely unclear. Studies have found that the subcortical frequency-following response to the fundamental frequency of speech and its higher harmonics (speech-FFR) may be involved since it is larger in people with musical training than in those without. Recent research has shown that the speech-FFR consists of a cortical contribution in addition to the subcortical sources. Both the subcortical and the cortical contribution are modulated by selective attention to one of two competing speakers. However, it is unknown whether the strength of the cortical contribution to the speech-FFR, or its attention modulation, is influenced by musical training. Here we investigate these issues through magnetoencephalographic (MEG) recordings of 52 subjects (18 musicians, 25 non-musicians, and 9 neutral participants) listening to two competing male speakers while selectively attending one of them. The speech-in-noise comprehension abilities of the participants were not assessed. We find that musicians and non-musicians display comparable cortical speech-FFRs and additionally exhibit similar subject-to-subject variability in the response. Furthermore, we also do not observe a difference in the modulation of the neural response through selective attention between musicians and non-musicians. Moreover, when assessing whether the cortical speech-FFRs are influenced by particular aspects of musical training, no significant effects emerged. Taken together, we did not find any effect of musical training on the cortical speech-FFR.

Evidence for a Causal Dissociation of the McGurk Effect and Congruent Audiovisual Speech Perception via TMS to the Left pSTS.

Congruent visual speech improves speech perception accuracy, particularly in noisy environments. Conversely, mismatched visual speech can alter what is heard, leading to an illusory percept that differs from the auditory and visual components, known as the McGurk effect. While prior transcranial magnetic stimulation (TMS) and neuroimaging studies have identified the left posterior superior temporal sulcus (pSTS) as a causal region involved in the generation of the McGurk effect, it remains unclear whether this region is critical only for this illusion or also for the more general benefits of congruent visual speech (e.g., increased accuracy and faster reaction times). Indeed, recent correlative research suggests that the benefits of congruent visual speech and the McGurk effect rely on largely independent mechanisms. To better understand how these different features of audiovisual integration are causally generated by the left pSTS, we used single-pulse TMS to temporarily disrupt processing within this region while subjects were presented with either congruent or incongruent (McGurk) audiovisual combinations. Consistent with past research, we observed that TMS to the left pSTS reduced the strength of the McGurk effect. Importantly, however, left pSTS stimulation had no effect on the positive benefits of congruent audiovisual speech (increased accuracy and faster reaction times), demonstrating a causal dissociation between the two processes. Our results are consistent with models proposing that the pSTS is but one of multiple critical areas supporting audiovisual speech interactions. Moreover, these data add to a growing body of evidence suggesting that the McGurk effect is an imperfect surrogate measure for more general and ecologically valid audiovisual speech behaviors.

Hidden hearing loss in a Charcot-Marie-Tooth type 1A mouse model.

Hidden hearing loss (HHL), a recently described auditory neuropathy characterized by normal audiometric thresholds but reduced sound-evoked cochlear compound action potentials, has been proposed to contribute to hearing difficulty in noisy environments in people with normal hearing thresholds, a widespread complaint. While most studies on HHL pathogenesis have focused on inner hair cell (IHC) synaptopathy, we recently showed that transient auditory nerve (AN) demyelination also causes HHL in mice. To test the impact of myelinopathy on hearing in a clinically relevant model, we studied a mouse model of Charcot-Marie-Tooth type 1A (CMT1A), the most prevalent hereditary peripheral neuropathy in humans. CMT1A mice exhibited the functional hallmarks of HHL together with disorganization of AN heminodes near the IHCs with minor loss of AN fibers. These results support the hypothesis that mild disruptions of AN myelination can cause HHL, and that heminodal defects contribute to the alterations in the sound-evoked cochlear compound action potentials seen in this mouse model. Also, these findings suggest that patients with CMT1A or other mild peripheral neuropathies are likely to suffer from HHL. Furthermore, these results suggest that studies of hearing in CMT1A patients might help develop robust clinical tests for HHL, which are currently lacking.

A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA.

In recent years, single-source-data-based deep learning methods have made considerable strides in the field of fault diagnosis. Nevertheless, the extraction of useful information from multi-source data remains a challenge. In this paper, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with a multi-source data convolutional neural network (MSCNN) for the fault diagnosis of rolling bearing. This method aims to identify bearing faults more accurately and make full use of multi-source data. Initially, the bearing vibration signal is transformed into a time-frequency graph using the continuous wavelet transform (CWT) and the signal is integrated with the motor current signal and fed into the network model. Then, a GASA-MSCNN fault diagnosis method is established to better capture the crucial information within the signal and identify various bearing health conditions. Finally, a rolling bearing dataset under different noisy environments is employed to validate the robustness of the proposed model. The experimental results demonstrate that the proposed method is capable of accurately identifying various types of rolling bearing faults, with an accuracy rate reaching up to 98% or higher even in variable noise environments. The experiments reveal that the new method significantly improves fault detection accuracy.

Mating with Multi-Armed Bandits: Reinforcement Learning Models of Human Mate Search.

Mate choice requires navigating an exploration-exploitation trade-off. Successful mate choice requires choosing partners who have preferred qualities; but time spent determining one partner's qualities could have been spent exploring for potentially superior alternatives. Here I argue that this dilemma can be modeled in a reinforcement learning framework as a multi-armed bandit problem. Moreover, using agent-based models and a sample of k = 522 real-world romantic dyads, I show that a reciprocity-weighted Thompson sampling algorithm performs well both in guiding mate search in noisy search environments and in reproducing the mate choices of real-world participants. These results provide a formal model of the understudied psychology of human mate search. They additionally offer implications for our understanding of person perception and mate choice.

Effectiveness of Noise Cancelling Earbuds in Reducing Hearing and Auditory Attention Deficits in Children with Autism.

Background/Objectives: Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition characterised by impairments in social communication, sensory abnormalities, and attentional deficits. Children with ASD often face significant challenges with speech perception and auditory attention, particularly in noisy environments. This study aimed to assess the effectiveness of noise cancelling Bluetooth earbuds (Nuheara IQbuds Boost) in improving speech perception and auditory attention in children with ASD. Methods: Thirteen children aged 6-13 years diagnosed with ASD participated. Pure tone audiometry confirmed normal hearing levels. Speech perception in noise was measured using the Consonant-Nucleus-Consonant-Word test, and auditory/visual attention was evaluated via the Integrated Visual and Auditory Continuous Performance Task. Participants completed these assessments both with and without the IQbuds in situ. A two-week device trial evaluated classroom listening and communication improvements using the Listening Inventory for Education-Revised (teacher version) questionnaire. Results: Speech perception in noise was significantly poorer for the ASD group compared to typically developing peers and did not change with the IQbuds. Auditory attention, however, significantly improved when the children were using the earbuds. Additionally, classroom listening and communication improved significantly after the two-week device trial. Conclusions: While the noise cancelling earbuds did not enhance speech perception in noise for children with ASD, they significantly improved auditory attention and classroom listening behaviours. These findings suggest that Bluetooth earbuds could be a viable alternative to remote microphone systems for enhancing auditory attention in children with ASD, offering benefits in classroom settings and potentially minimising the stigma associated with traditional assistive listening devices.

Agreeing to disagree: Informativeness of sentiments in internet message boards

We study the informativeness of the sentiment of posts on Australia's largest online stock message board, HotCopper. We explore whether disagreements among sentiment form a signal about the fundamental news of a company. We find that positive sentiment is associated with noise induced (uniformed) trading whereas negative sentiment contains value-relevant information about a firm's performance. Our empirical findings suggest that short selling activity reduces overreactions of abnormal returns in a noisy environment on the same day. Furthermore, we observe a sentiment convergence pattern around annual earnings announcements and low levels of sentiment homogeneity relate to significantly lower annual earnings surprise. This supports the view that disagreements among sentiments are a signal of bad news about firm fundamentals.

Noise robustness evaluation of image processing algorithms for eye blinking detection

Robust algorithms for eye blinking detection are required due to the effects of noisy environments and varying light conditions on image-based detection methods. This paper compares five non-supervised image-based algorithms for eye blinking detection, evaluating their robustness to additive Gaussian noise. The algorithms were tested on a video dataset acquired using a smartphone and an ophthalmology chin rest. Through Monte Carlo simulation that introduces Gaussian noise at different intensities, we evaluate the algorithms’ precision, sensitivity, and F1-scores for frame classification, True Positive Rate (TPR), False Discovery Rate (FDR) and F1-score for the event detector. The results of experimental tests reveal significant variations in algorithms’ performance with increasing noise levels. Notably, the Image Correlation (IC) algorithm demonstrates superior eye blinking detection capabilities under various noise conditions, emerging as the most robust algorithm among those tested. This distinction highlights the potential of IC for reliable blink detection in noisy environments.

Rolling bearing fault diagnosis method based on multi-scale pooling residual convolutional neural network under noisy environment

To address the issues of unstable performance and poor generalization ability of bearing fault diagnosis model caused by strong noise and variable operating conditions in actual production，this paper proposes a rolling bearing fault diagnosis method based on MSPRCNN model. By converting vibration signals to frequency domain with FTand utilizing wide convolution kernels for feature extraction, the approach aims to enhance fault detection. A MSPFE module captures information at different scales to simplify complexity, while an UPA module establishes correlations between frequency domain positions. To reduce the impact of noise and address the vanishing gradient problem, the MSPRCNN model employs GC instead of standard convolution, and utilizes the IReLU activation function to improve model feature representation. Experimental results on two datasets show that the fault recognition accuracy is 98.71% under variable loads and 98.2% under variable speeds. The MSPRCNN model outperforms other methods in fault recognition and generalization in noisy environments.