Acoustic Signals Research Articles

People follow motivation-structural rules when they react to synthetised sounds

Emotionally expressive vocalizations can elicit approach-avoidance responses in humans and non-human animals. We investigated whether artificially generated sounds have similar effects on humans. We assessed whether subjects' reactions were linked to acoustic properties, and associated valence and intensity. We generated 343 artificial sounds with differing call lengths, fundamental frequencies and added acoustic features across 7 categories and 3 levels of biological complexity. We assessed the hypothetical behavioural response using an online questionnaire with a manikin task, in which 172 participants indicated whether they would approach or withdraw from an object emitting the sound. (1) Quieter sounds elicited approach, while loud sounds were associated with avoidance. (2) The effect of pitch was modulated by category, call length and loudness. (2a) Low-pitched sounds in complex sound categories prompted avoidance, while in other categories they elicited approach. (2b) Higher pitch in loud sounds had a distancing effect, while higher pitch in quieter sounds prompted approach. (2c) Longer sounds promoted avoidance, especially at high frequencies. (3) Sounds with higher intensity and negative valence elicited avoidance. We conclude that biologically based acoustic signals can be used to regulate the distance between social robots and humans, which can provide an advantage in interactive scenarios.

A Non-Metallic pipeline leak size recognition method based on CWT acoustic image transformation and CNN

Accurately identifying the pipeline leak size is crucial for risk assessment and timely rescue. In this study, a Convolutional Neural Network (CNN) based on Continuous Wavelet Transform (CWT) acoustic image transformation is proposed to identify small-sized leak in non-metallic pipes. Firstly, one-dimensional acoustic signals are filtered using the Piecewise Aggregate Approximation (PAA) algorithm to reduce noise and storage resource consumption. Then, the filtered signals are transformed into two-dimensional images by CWT to enrich signal feature information, serving as the input for the CNN. Further, a leak size recognition model based on CWT-CNN is established. The effectiveness of this model is verified using experimental data from a non-metallic pipeline leak test. A comparative analysis is conducted on diverse acoustic image transformation methods, including CWT, Gramian Angular Summation Field (GASF), and Relative Position Matrix (RPM). The results demonstrate the superiority of the CWT-CNN model in pipeline leak size recognition. Finally, the impact of the signal length in an acoustic image on recognition accuracy is also examined. The results demonstrate that when the signal length in an acoustic image is 0.75 s, the accuracy obtained by CWT-CNN can reach 95 %.

Acoustic emission monitoring of composite marine propellers in submerged conditions using embedded piezoelectric sensors

Abstract Flexible composite marine propellers can aid the marine industry in reducing carbon emissions and underwater radiated noise pollution. The structural integrity of the blades can be assessed using structural health monitoring. One of these methods is the measurement and analysis of damage-induced acoustic emission signals. This paper experimentally investigates the feasibility of using embedded piezoelectric sensors for the measurement of acoustic emissions throughout a submerged flexible composite marine propeller blade. A full-scale glass-fibre reinforced polymer blade has been manufactured with 24 embedded sensors. While suspended in artificial seawater, acoustic emissions were simulated on the blade. The measurements show that the embedded piezoelectric sensors can measure acoustic emissions while the blade is submerged. Further, the distance from source to sensor over which the acoustic emission is measurable was investigated. For a noise level of 40dB and a source amplitude of 70dB between 100-250kHz, an average maximum measurable distance of 124mm was obtained. For higher frequencies, the distance drops and for lower noise levels the distance increases.

Technology for Sensing and Reducing Noise in Acoustic Emission Signals for Online Detection of Wear Condition of Transmission Gears in a Nuclear Power Plant

Technology for Sensing and Reducing Noise in Acoustic Emission Signals for Online Detection of Wear Condition of Transmission Gears in a Nuclear Power Plant

Two-dimensional layered material photodetectors: what could be the upcoming downstream applications beyond prototype devices?

With distinctive advantages spanning excellent flexibility, rich physical properties, strong electrostatic tunability, dangling-bond-free surface, and ease of integration, 2D layered materials (2DLMs) have demonstrated tremendous potential for photodetection. However, to date, most of the research enthusiasm has been merely focused on developing novel prototype devices. In the past few years, researchers have also been devoted to developing various downstream applications based on 2DLM photodetectors to contribute to promoting them from fundamental research to practical commercialization, and extensive accomplishments have been realized. In spite of the remarkable advancements, these fascinating research findings are relatively scattered. To date, there is still a lack of a systematic and profound summarization regarding this fast-evolving domain. This is not beneficial to researchers, especially researchers just entering this research field, who want to have a quick, timely, and comprehensive inspection of this fascinating domain. To address this issue, in this review, the emerging downstream applications of 2DLM photodetectors in extensive fields, including imaging, health monitoring, target tracking, optoelectronic logic operation, ultraviolet monitoring, optical communications, automatic driving, and acoustic signal detection, have been systematically summarized, with the focus on the underlying working mechanisms. At the end, the ongoing challenges of this rapidly progressing domain are identified, and the potential schemes to address them are envisioned, which aim at navigating the future exploration as well as fully exerting the pivotal roles of 2DLMs towards the practical optoelectronic industry.

A Fault Diagnosis Approach for Flange Stabilizer Based on Multi-Signal Fusion

Abstract As an essential means of energy transportation, pipelines have been widely used in various fields. However, many external factors such as vibration and corrosion can cause damage at the flange part, which seriously affects the safety of pipeline transportation. Quite a number of methods for troubleshooting at pipeline flanges have been continuously proposed, yet there is little research on diagnostic methods for the stabilizer at the flange. Therefore, in this paper, we focus on the stabilizer of the flange and a method that combines traditional detection and machine learning with each other to detect stabilizer faults is proposed. At first, we can obtain a stable and reliable diagnostic data by combining the advantages of the preload of the bolt and the acoustic signal. Subsequently, the optimized N-Beats model is trained based on the measured bolt preload data to predict the service state of the stabilizer. Finally, the data measured by the sensors as well as the predicted data are analyzed by a simplified classification algorithm to determine whether a fault has occurred and to classify the fault. The fault detection method used in this paper not only improves the accuracy of detection and shortens the fault detection time, but also improves the automation level of pipeline inspection. Hence, the work done in this paper has far-reaching practical significance for ensuring the safe and stable operation of pipelines.

Experimental research on damage characteristics of red sandstone under the combined action of temperature, water and stress

In order to study the damage properties of red sandstone under combined action of temperature, water and stress, the uniaxial compression test was carried out for red sandstone after immersing in water for 0.5 h, 1 h and 3 h at 25 ℃, 55 ℃, 85 ℃ and 95 ℃, the mechanical properties, characteristic stress points, acoustic emission RA-AF parameters and failure forms of red sandstone were analyzed, the damage constitutive equation was established, and the damage properties of red sandstone were studied. The results showed: (1) temperature could significantly improve the saturated water content percentage of red sandstone. With the increase of temperature, the saturated water content percentage showed a trend of increase first and then decrease, and was the largest at 55 ℃; with the increase of temperature, the mechanical parameters (peak intensity, elasticity modulus, and stress at start point of expansion) decreased first and then increased, and was the smallest at 55 ℃; (2) the ratio of crack initiation stress to peak stress (σci/σf) initially increases and then decreases with rising temperatures for the same soaking duration. Conversely, it initially decreases and then increases with extended soaking time at the same temperature. The RA-AF acoustic emission signals predominantly occur in the tensile region at each stage account for a relatively large proportion, with the proportion of signals in the shear region significantly increasing as soaking time extends, corresponding well with the observed damage patterns. (3) By considering the damage due to temperature and water as Initial damage to red sandstone and the damage due to stress as load-induced damage, a damage variable based on the Weibull distribution function was established. The comprehensive damage constitutive equation derived from this can be applied to water-saturated red sandstone below 100 ℃. The findings provide theoretical reference for practical engineering applications.

Collective dynamics of liquid sulphur across the polymerisation transition temperature probed by inelastic x-ray scattering

Inelastic x-ray scattering (IXS) experiments on liquid sulphur were carried out below (140 ∘C) and above (180 ∘C) the polymerisation temperature T λ of about 159 ∘C to investigate changes in the collective dynamics of this unique liquid, that exhibits a liquid–liquid transition. As reported earlier, broad longitudinal acoustic excitation signals were observed at both temperatures, and only the width of the quasielastic peaks slightly decreased when the temperature crossed the transition temperature. A model analysis was performed using a generalised Langevin formalism with a memory function having one thermal and two viscoelastic decay channels with the help of simple sparse modelling, and large positive deviations from the hydrodynamic sound velocity by 51%–54% were observed. The fast viscoelastic relaxation time τ µ is close to the correlation times of intermolecular stretching and bending motions of local sulphur connections in both ring and chain structures, and is similar to those of other molecular liquids. The small contrasts in the IXS spectra across the λ transition result in large changes in only the slow viscoelastic decay time τ α of the memory function. The τ α value at 140 ∘C matches the mixed internal/external torsional modes of S8 molecules well, whereas that at 180 ∘C has no corresponding molecular motion mode. The kinematic viscosity values at the Q→0 limit are much smaller than the minimum values of macroscopic shear viscosity, indicating that large changes in relaxation dynamics are expected with Q in the GHz and MHz excitation regimes.

Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations

Abstract/Summary The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultra-high field functional magnetic resonance imaging (fMRI) acquisition have enabled novel non-invasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.

Acoustic Sensors for Monitoring and Localizing Partial Discharge Signals in Oil-Immersed Transformers under Array Configuration.

Partial discharge (PD) is one of the major causes of insulation accidents in oil-immersed transformers, generating a large number of signals that represent the health status of the transformer. In particular, acoustic signals can be detected by sensors to locate the source of the partial discharge. However, the array, type, and quantity of sensors play a crucial role in the research on the localization of partial discharge sources within transformers. Hence, this paper proposes a novel sensor array for the specific localization of PD sources using COMSOL Multiphysics software 6.1 to establish a three-dimensional model of the oil-immersed transformer and the different defect types of two-dimensional models. "Electric-force-acoustic" multiphysics field simulations were conducted to model ultrasonic signals of different types of PD by setting up detection points to collect acoustic signals at different types and temperatures instead of physical sensors. Subsequently, simulated waveforms and acoustic spatial distribution maps were acquired in the software. These simulation results were then combined with the time difference of arrival (TDOA) algorithm to solve a system of equations, ultimately yielding the position of the discharge source. Calculated positions were compared with the actual positions using an error iterative algorithm method, with an average spatial error about 1.3 cm, which falls within an acceptable range for fault diagnosis in transformers, validating the accuracy of the proposed method. Therefore, the presented sensor array and computational localization method offer a reliable theoretical basis for fault diagnosis techniques in transformers.

High-frequency hearing sensitivity does not systematically imply aversion to ultrasounds in large terrestrial mammals

ABSTRACT Crop damage and vehicle collisions caused by wild ungulates require effective deterrents. Many commercial acoustic devices use ultrasonic signals, presenting them as aversive to these animals. However, unconvincing results reported in previous studies using (non-frequency modulated) ultrasounds bring their effectiveness as deterrent signals into question. Here, we explored the deterrent effectiveness of ultrasounds in several large mammalian species that are expected to perceive these high frequencies. Specifically, we tested the effect of different ultrasonic signals on red deer, peccaries, and wolves. Animals were exposed to sequences of nine acoustic signals in the 20–30 kHz frequency range, and to a lower-pitched signal (2–3 kHz). While red deer showed no aversion to ultrasonic signals (inaudible for humans), lower frequency (audible for humans) modulations increased aversive responses. Conversely, peccaries showed strong flight responses to several ultrasonic signals. Wolves also reacted to ultrasounds but did not flee. Our study confirms that ultrasonic signals are not suitable for deterring European red deer. However, our results suggest that their effectiveness in other species should be systematically tested, without assuming that their hearing frequency range is a reliable predictor of their behavioural responses to ultrasonic signals.

A Method for Semi-automatic Mode Recognition in Acoustic Emission Signals

A Method for Semi-automatic Mode Recognition in Acoustic Emission Signals

Research on wavelet packet energy entropy extraction method for acoustic signal of tunnel lining cavity

Abstract Tunnel lining cavity is a common disease in high-speed railway tunnels, which seriously affects the safe operation of the line.At present, the commonly used method is manual knocking inspection, but it relies on manual judgment, subjective factors have great influence, and there are errors. This paper proposes a feature extraction method of acoustic vibration signal of tunnel lining cavity based on wavelet packet energy entropy and uses wavelet packet energy entropy to realize the identification of cavity defects.The full-scale simulation model of tunnel with a radius of 6m was constructed by COMSOL and different cavity conditions were set up. The wavelet packet energy entropy characteristics of acoustic signals were extracted and analyzed. The mapping relationship between cavity depth and cavity size and wavelet packet energy entropy was explored. The wavelet packet energy entropy and frequency band distribution characteristics were extracted to realize the identification of cavity defects.The local model which can characterize the whole tunnel is designed and the embedded defects are verified by experiments. The results show that the wavelet packet energy entropy can be used as the identification feature of tunnel lining cavity defects. When the wavelet packet energy entropy is less than 2.53, the existence of tunnel lining cavity defects is characterized.When the wavelet packet energy entropy is in the range of 2.53-2.94, it characterizes the existence of the transition boundary between tunnel lining cavity defect and no disease.This index can be used to realize the rapid and effective identification of cavity defects.

Investigation of calendaring parameters on the microstructure of graphite anodes within lithium-ion batteries: Insights from ultrasonic testing

The study explores the application of ultrasonic testing to analyse the impact of the calendaring process on the microstructure of graphite anodes within a lithium-ion battery. It examines the correlation between calendaring parameters such as roll gap and line speed, and their impacts on ultrasonic signal characteristics.Our study reveals that different sample thicknesses responded differently to the calendaring process. Specifically, the amplitude of the first peak increased for thinner samples (<45 μm) post-calendaring, indicating improved material uniformity, whereas thicker samples (>50 μm) demonstrated increase in Time of Flight with reduced attenuation in acoustic signal. Notably, thinner electrodes (45 μm) exhibit a substantial density increase from 0.95 to 1.6 gr/cm³, surpassing the density gain in thicker electrodes (100 μm) which rise from 1.0 to 1.3 gr/cm³ under identical calendaring conditions.This initial study confirms that a clear correlation exists between the properties of the acoustic waveform and variations in the calendaring process parameters. These results will form the basis of a future study investigating the possible in-line sensing, process control and quality assessment of electrode manufacture. The data obtained from this study's characterisation process has the potential to underpin a data-driven model to predict calendaring performance using machine-learning methods.

Biologically inspired piezoresistive MEMS acoustic vector sensor for underwater applications

Underwater acoustics has assumed a pivotal role in the detection, navigation, communication, and target identification of ships and submarines. In recent years, advancements in noise reduction from marine vessels have underscored the significance of underwater acoustic vector sensors. This paper presents a novel approach to the design, fabrication, and characterization of a two-dimensional Micro-Electro Mechanical Systems (MEMS) vector acoustic sensor tailored for underwater environments. Traditional underwater acoustic vector sensors often employ doped polysilicon or silicon/germanium sensing films on rigid silicon substrates. The inflexible nature of silicon substrates necessitates labor-intensive and costly chemical-mechanical polishing processes to achieve submicron thickness, often requiring extended clean-room usage. In contrast, this work proposes a simplified, cost-effective, and scalable alternative. The sensor design utilizes reduced graphene oxide (rGO) as the sensing material and Kapton as the substrate, chosen for their unique properties. rGO exhibits high carrier mobility and low intrinsic noise, ensuring clear signals with minimal external interference, thereby improving measurement accuracy in challenging environments. Kapton provides excellent thermal stability, mechanical strength, and chemical resistance, along with superior insulating properties that reduce electrical noise, enhancing the signal-to-noise ratio. The designed vector sensor leverages the piezoresistive transduction principle, incorporating a thin piezoresistive film of rGO on a flexible Kapton substrate via a straightforward drop-casting method. Inspired by the sensory mechanisms of aquatic life, the two-dimensional vector sensor not only detects pressure associated with acoustic signals but also discerns the direction from which these low and ultralow frequency signals originate. The device's performance characteristics, sensitivity, and directivity are comprehensively evaluated through static, dynamic, and acoustic tests conducted in both air and water mediums. Static tests reveal the piezoresistive behavior of rGO under applied displacement, yielding displacement sensitivities of 0.0279 V/mm and 0.0276 V/mm for axial and radial displacements, respectively. Dynamic and acoustic tests demonstrate the sensor's capability to detect low-frequency signals spanning from 0.25 Hz to 200 Hz, exhibiting a smooth and favorable frequency response. In air, the sensor exhibits receiving sensitivities ranging from −15.18 dB to −19.75 dB (X-channel) and −13.67 dB to −16.67 dB (Y-channel). In the underwater environment, these values range from −134.40 dB to −138.93 dB (X-channel) and −131.83 dB to −136.93 dB (Y-channel). Notably, the variation in receiving sensitivity across channels in both air and water is limited to within 6 dB. Furthermore, the device showcases a symmetrical directivity pattern resembling the shape of an "8," with an approximate sensitivity of −136.66 dB, a significant improvement compared to the −165 dB sensitivity of conventional polysilicon underwater vector sensors. These experimental findings affirm the feasibility of the proposed underwater vector sensor, based on the piezoresistive effect and MEMS technology, highlighting its two-dimensional directivity capabilities.

Dynamic stress characterization and instability risk identification using multisource acoustic signals in cut-and-fill stopes

The quantitative characterization of rock mass and stress changes induced by mining activities is crucial for structural stability monitoring and disaster early warning. This paper investigates the time–space–intensity distribution of microseismic sources during the pillar-free large-area continuous extraction. Furthermore, it explores a method involving collaborative evolution patterns of the velocity field and spatial b-value to identify stress and structural changes at the panel stope. Results show that anomalous zones in wave velocities and b-values form at the intersections of extraction drifts, strike drifts, cross drifts, and connection roadways influenced by mining activities, as well as in footwall ore-rock contacts, often accompanied by the nucleation of microseismic events. The synergistic use of wave velocity fields and spatial b-value models reveals the relationship between stress migration behavior and stope structure changes due to mining disturbances. The velocity field primarily reflects macroscopic changes in the structure and stress distribution, while spatial b-values further explain stress gradients in specific areas. Additionally, we have advanced the identification of an instability disaster at the connection roadway and cross drift intersection based on increases in wave velocity and abnormal changes in b-value. This paper demonstrates the potential of risk identification using the proposed method, providing insights into predicting geotechnical engineering disasters in complex stress environments.

Towards Smartphone-based 3D Hand Pose Reconstruction Using Acoustic Signals

Accurately reconstructing 3D hand poses is a pivotal element for numerous Human-Computer Interaction applications. In this work, we propose SonicHand, the first Smartphone-based 3D Hand Pose Reconstruction system using purely inaudible acoustic signals. SonicHand incorporates signal processing techniques and a deep learning framework to address a series of challenges. Firstly, it encodes the topological information of the hand skeleton as prior knowledge and utilizes a deep learning model to realistically and smoothly reconstruct the hand poses. Secondly, the system employs adversarial training to enhance the generalization ability of our system to be deployed in a new environment or for a new user. Thirdly, we adopt a hand tracking method based on channel impulse response (CIR) estimation. It enables our system to handle the scenario where the hand performs gestures while moving arbitrarily as a whole. We conduct extensive experiments on a smartphone testbed to demonstrate the effectiveness and robustness of our system from various dimensions. The experiments involve 10 subjects performing up to 12 different hand gestures in 3 distinctive environments. When the phone is held in one of the user’s hand, the proposed system can track joints with an average error of 18.64 mm.

Modulation recognition of underwater acoustic communication signals based on neural architecture search

Modulation recognition of underwater communication signals is a critical aspect of underwater information confrontation. However, the current deep learning-based methods for underwater communication modulation recognition often mimic neural network architectures used in image processing and speech processing, tending to perform poorly in low signal-to-noise ratio (SNR) conditions. This paper introduces a novel approach by utilizing neural architecture search (NAS) method. By automatically searching for neural architectures that are well-suited for modulation recognition in underwater environment, our proposed method improves the classification performance particularly in low SNR conditions. Furthermore, we have also proposed a recognition method based on attention mechanism and feature fusion, which substantially enhances the accuracy of identifying phase-modulated signals. Numerical simulation and experimental data are used to demonstrate performance of proposed methods.

Lung quantitative ultrasound to stage and monitor interstitial lung diseases

Chronic interstitial lung diseases (ILDs) require frequent point-of-care monitoring. X-ray-based methods lack resolution and are ionizing. Chest computerized tomographic (CT) scans are expensive and provide more radiation. Conventional ultrasound can detect severe lung damage via vertical artifacts (B-lines). However, this information is not quantitative, and the appearance of B-lines is operator- and system-dependent. Here we demonstrate novel ultrasound-based biomarkers to assess severity of ILDs. Lung alveoli scatter ultrasound waves, leading to a complex acoustic signature, which is affected by changes in alveolar density due to ILDs. We exploit ultrasound scattering in the lung and combine quantitative ultrasound (QUS) parameters, to develop ultrasound-based biomarkers that significantly correlate (p = 1e−4 for edema and p = 3e−7 for fibrosis) to the severity of pulmonary fibrosis and edema in rodent lungs. These innovative QUS biomarkers will be very significant for monitoring severity of chronic ILDs and response to treatment, especially in this new era of miniaturized and highly portable ultrasound devices.

Direct mechanistic connection between acoustic signals and melt pool morphology during laser powder bed fusion

Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. In this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.