Acoustic Signals Research Articles

Diffraction efficiency in acousto-optical interaction with short radio pulses

Development of acousto-optic modulators with high diffraction efficiency, which is achieved by using multi-frequency acoustic signals, is of great interest. In this regard, it seems interesting to study the possibility of implementing highly efficient diffraction for wideband signals in the form of radio pulses with short duration. The goal of presented article is to develop a mathematical model based on a quantum description of the interaction between a photon and a phonon, simulate various operating modes of an acousto-optical modulator and compare theoretical results with experimental ones. A comparison between experimental and theoretical results was made, demonstrating the possibility of obtaining high diffraction efficiency (more than 90%) for wideband signals. The proposed method makes it possible to determine the conditions under which maximum diffraction efficiency is observed, and it also seems possible to determine the parameters of the pulse sequence.

Detection of Typical Transient Signals in Water by XGBoost Classifier Based on Shape Statistical Features: Application to the Call of Southern Right Whale

Whale sound is a typical transient signal. The escalating demands of ecological research and marine conservation necessitate advanced technologies for the automatic detection and classification of underwater acoustic signals. Traditional energy detection methods, which focus primarily on amplitude, often perform poorly in the non-Gaussian noise conditions typical of oceanic environments. This study introduces a classified-before-detect approach that overcomes the limitations of amplitude-focused techniques. We also address the challenges posed by deep learning models, such as high data labeling costs and extensive computational requirements. By extracting shape statistical features from audio and using the XGBoost classifier, our method not only outperforms the traditional convolutional neural network (CNN) method in accuracy but also reduces the dependence on labeled data, thus improving the detection efficiency. The integration of these features significantly enhances model performance, promoting the broader application of marine acoustic remote sensing technologies. This research contributes to the advancement of marine bioacoustic monitoring, offering a reliable, rapid, and training-efficient method suitable for practical deployment.

A Dual-Stream Deep Learning-Based Acoustic Denoising Model to Enhance Underwater Information Perception

Estimating the line spectra of ship-radiated noise is a crucial remote sensing technique for detecting and recognizing underwater acoustic targets. Improving the signal-to-noise ratio (SNR) makes the low-frequency components of the target signal more prominent. This enhancement aids in the detection of underwater acoustic signals using sonar. Based on the characteristics of low-frequency narrow-band line spectra signals in underwater target radiated noise, we propose a dual-stream deep learning network with frequency characteristics transformation (DS_FCTNet) for line spectra estimation. The dual streams predict amplitude and phase masks separately and use an information exchange module to swap learn features between the amplitude and phase spectra, aiding in better phase information reconstruction and signal denoising. Additionally, a frequency characteristics transformation module is employed to extract convolutional features between channels, obtaining global correlations of the amplitude spectrum and enhancing the ability to learn target signal features. Through experimental analysis on ShipsEar, a dataset of underwater acoustic signals by hydrophones deployed in shallow water, the effectiveness and rationality of different modules within DS_FCTNet are verified.Under low SNR conditions and with unknown ship types, the proposed DS_FCTNet model exhibits the best line spectrum enhancement compared to methods such as SEGAN and DPT_FSNet. Specifically, SDR and SSNR are improved by 14.77 dB and 13.58 dB, respectively, enabling the detection of weaker target signals and laying the foundation for target localization and recognition applications.

Auditory System of Blood-Sucking Mosquitoes (Diptera, Culicidae)

This review comprehensively explores the morphology of the mosquito auditory organs – antennae and Johnston’s organs. Spatial and frequency characteristics of auditory sensory neurons within Johnston’s organs are discussed, as well as the mechanisms of mechanotransduction in these neurons. The review presents findings from studies investigating the aspects of mosquito perception of acoustic signals when their auditory system is subjected to vibrations generated by flapping wings. Additionally, the review discusses the significance of acoustic communication in the reproductive behavior of mosquitoes.

Unsupervised weathering identification of grottoes sandstone via statistical features of acoustic emission signals and graph neural network

Weathering features of sandstone heritage can be recognized by using artificial intelligence (AI) based surrogate models, and most models perform classification tasks for types based on precise labels. But there are lack of prior validated knowledge of the weathering or untagged historical data for complex weathering conditions in many cases. To this aim, a unsupervised graph neural network (GNN) based on the statistical features of the acoustic emission (AE) signals is constructed. Firstly, taking unweathered sandstone as a reference, we define 4 weathering levels of sandstone ranging from I to IV based on pore indicators. We selected 11 statistical features that are high correlated with pore of sandstone. Then, this GNN is constructed and trained by 2880 sets of statistical measured AE signals. Compared with AEs, LOF and IF models, GNN achieves the best identification performance among the four evaluation criteria. Each iteration of the GNN network is fitting the feature information of the signals and their neighbors. By data dimensionality reduction techniques, when the GNN stops iterating, it will be easy to distinguish unweathered AE signals from weathered one by comparing the reconstruction error of each signal. Furthermore, when the nearest neighbor’s k gradually increases, the AUC of GNN also gradually increases and then tend to stable when k equals to 50–100. While the hidden layers of the network aggregates less information about the neighborhood features of the signals and cannot distinguish significantly between unweathered and weathered signals when the value of k is small. As the depth of the network deepens, the feature values between signals become more and more similar, their reconstruction errors in the output layer of the network to become more similar, making it difficult to distinguish unweathered AE signals from weathered AE signals via GNN. Meanwhile, GNN adopts more AE features and considers the similarity between each features. This can greatly eliminate various errors caused by wave velocity measurement, greatly improving the robustness of AE detection. Hence, the GNN model presented addresses the limitations of relying solely on P-wave velocity measurements to assess the degree of sandstone weathering at stone cultural heritage.

Machine learning based damage identification in SiC/SiC composites from acoustic emissions using autoencoders

Developing the ability to leverage machine learning (ML) to identify damage mechanisms in heterogeneous materials from their acoustic emissions (AE) has wide-reaching ramifications for multi-modal experimentation. It would allow researchers to augment damage triangulation, lifetime prediction, and high-resolution optical studies with complementary mechanism-informed data streams. However, developing this capability hinges on the collection of ground truth acoustic libraries from damage in realistic geometries. Due to time and monetary considerations, there is a dearth of ground truth libraries which can be used to robustly characterize ML mechanism identification frameworks. Addressing this gap, we present a multi-modal acoustic emission and x-ray computed tomography study where AE is gathered, and subsequently labeled, from SiC/SiC unidirectional composites under monotonic tension. This library is used to demonstrate that acoustic signals from early fiber breaks are obscured by matrix cracking. A first-order micromechanical model is used to explain the origin of this obscuring effect, and identify fundamental limitations of unsupervised frameworks. An autoencoder-based anomaly detector approach is used for the first time to overcome these limitations, additionally demonstrating that the frequency distribution of fiber break acoustic signals is narrow. Implications of these findings for enhanced multi-modal testing and online health monitoring are discussed, and strategies for implementation of supervised damage mechanism identification frameworks are proposed.

Detecting weak underwater acoustic signal with the combination of composite second-order tristable coupled stochastic resonance and improved variational mode decomposition

Detecting weak underwater acoustic signal with the combination of composite second-order tristable coupled stochastic resonance and improved variational mode decomposition

The mechanics of male courtship display behaviour in the Ptiloris riflebirds (Aves: Paradisaeidae)

Abstract Sexual selection through female choice has driven the evolution of some of the most elaborate signalling behaviours in animals. These displays often require specialized morphological adaptations and may incorporate signals in multiple sensory modalities. Visual and acoustic signals are often precisely choreographed in temporally structured courtship performances, though the precise mechanics of such signalling behaviours are often enigmatic. We find that riflebirds (genus Ptiloris)—a bird of paradise clade—achieve their remarkable display postures by hyperextending the wrist joint, vastly exceeding the maximal wrist extension capabilities of any other known bird. Using video collected in the field, we then show that this hypermobility is required for a sonation unique to riflebirds, and find that the yellow interior of the mouth is displayed in the dynamic phase of display. As this sonation cannot be produced when the mouth is exposed, it represents a mechanical constraint to signal design. Finally, we used a large morphometric dataset to describe patterns of sexual dimorphism in wing length across diverse bird of paradise species, and find evidence of sexual selection for large and structurally modified wings used in riflebird displays. Our study highlights nuanced choreographic differences in the display behaviours of different riflebird species, and sheds light on the intricate design features of sexual signals in this fascinating taxon.

The viscous-brittle transition in flowing crystal-bearing volcanic dome lavas

The ascent and advance of volcanic dome lava is non-linear and viscoelastic. There exists a mismatch between current theoretical approaches to dome lava rheology, which are based on rheological laws for viscous suspensions, and empirical experimental approaches to convolved viscous-brittle deformation, which show mixed evidence for simultaneous lava flow and fracturing. The missing requirement is a unified framework for understanding the transition between micro-mechanical flow mechanisms that are dominantly viscous, and those that include micro-cracking in multiphase suspensions such as magmas. Here, we use high-temperature compression rheology with sample-scale acoustic emission analysis to constrain the conditions under which crystal-rich volcanic dome lava can flow by mixed viscous and brittle fracturing processes at small scales, leading to ‘crackling’ acoustic signals, even at moderate shear stresses extant in nature. Using multi-directional permeability measurements on large 60 mm diameter quenched samples of natural magmas, we show that this micro-cracking flow mechanism leads to permeability anisotropy, localizing outgassing into pathways that are off-axis relative to the direction of flow. Finally, we use a scaling approach and a database of published observations from real eruptions to upscale our findings, and show that bulk, apparently ductile flow of low-porosity dome magma is likely to involve a local mixed-mode of micro-cracking and viscous flow during the shallowest portions of ascent and during emplacement on the Earth's surface. The micro-cracking involved in lava advance divorces real crystal-bearing lava emplacement from most current rheology models based on a purely viscous micro-mechanism and shows that a revised solution for the rheology of mixed brittle-viscous flow is required. By re-examining published numerical models for dome emplacement, we demonstrate that the viscous-brittle transition can be intercepted in spatially heterogeneous zones within the dome core.

Improvement Underwater Acoustic Signal De-Noising Based on Dual-Tree Complex Wavelet Transform

Underwater Acoustic signal denoising is encountering high demand due to the extensive use of acoustic in a lot of underwater applications. Underwater acoustic noise (UWAN) has a high effect on the quality of the acoustic signal therefore, it is always preferred to use a de-noising filter to remove it. In this paper, we propose a filter that utilizes a Complex wavelet transform (CWT) to remove UWAN and help improve the signal-to-noise ratio (SNR) of the detected acoustic signal. CWT is nearly shift-invariant and offers a good directionality in contrast to normal wavelet transform (DWT). The proposed method was tested using a real recorded UWAN for three depths from the Tigris River. The proposed method was compared with a more conveniently used discrete wavelet transform. The test included using Two signals: fixed frequency and linear modulation signal. De-noising was performed using a soft thresholding technique based on level-dependent threshold estimation. The proposed method showed supreme performance in terms of SNR and root mean square error (RMSE). When the input signal was 5.9 dB and -13.2 dB for SNR and RMSE respectively, the results were 10.9 dB for SNR and -15.7 dB for RMSE in the case of fixed frequency.

Aminolysis-mediated single-step surface functionalization of poly (butyl cyanoacrylate) microbubbles for ultrasound molecular imaging

Molecular ultrasound imaging with actively targeted microbubbles (MB) proved promising in preclinical studies but its clinical translation is limited. To achieve this, it is essential that the actively targeted MB can be produced with high batch-to-batch reproducibility with a controllable and defined number of binding ligands on the surface. In this regard, poly (n-butyl cyanoacrylate) (PBCA)-based polymeric MB have been used for US molecular imaging, however, ligand coupling was mostly done via hydrolysis and carbodiimide chemistry, which is a multi-step procedure with poor reproducibility and low MB yield. Herein, we developed a single-step coupling procedure resulting in high MB yields with minimal batch-to-batch variation. Actively targeted PBCA-MB were generated using an aminolysis protocol, wherein amine-containing cRGD was added to the MB using lithium methoxide as a catalyst. We confirmed the successful conjugation of cRGD on the MB surface, while preserving their structure and acoustic signal. Compared to the conventional hydrolysis protocol, aminolysis resulted in higher MB yields and better reproducibility of coupling efficiency. Optical imaging revealed that under flow conditions, cRGD- and rhodamine-labelled MB, generated by aminolysis, specifically bind to tumor necrosis factor-alpha (TNF-α) activated endothelial cells in vitro. Furthermore, US molecular imaging demonstrated a markedly higher binding of the cRGD-MB than of control MB in TNF-α activated mouse aortas and 4T1 tumors in mice. Thus, using the aminolysis based conjugation approach, important refinements on the production of cRGD-MB could be achieved that will facilitate the production of clinical-scale formulations with excellent binding and ultrasound imaging performance.Graphical

Acoustic wave propagation in depth-evolving sound-speed field using the lattice Boltzmann method

This study investigates the propagation of sound waves within deep-sea low-sound-speed channels using the lattice Boltzmann method, with a key focus on the influence of depth-dependent sound speed on wave propagation. The depth-variable sound speed condition is realized through the incorporation of an external force proportional to the density gradient. After the model verification, investigations into the two-dimensional spreading of sound sources reveal that the depth-dependent sound speed curves the wave propagation. When source depths differing from the low-sound-speed channel, wave paths deviate due to contrasting speeds above and below. When the sound source is situated within the low-sound-speed channel, waves exhibit converging patterns. The simulations also detail the total reflection behavior of sound waves. When the incident angle falls exceeds the critical angle, the waves remain intact within the low-sound-speed channel, thereby enabling the preservation of high amplitude acoustic signals even at remote locations. The subsequent simulations of sound wave propagation around obstacles demonstrate that the low-sound-speed channel also exhibits better signal transmission capabilities in the presence of obstacles. In a uniform sound speed environment, acoustic wave propagation around a submarine exhibits a symmetric pattern. By contrast, under depth-evolving speed conditions, submarines operating at various depths manifest distinct propagation characteristics, such as asymmetric wave propagation during shallow diving, as well as wave attenuation or even silencing when cruising within low-sound-speed channels. These findings underscore the profound implications of depth-evolving sound speed on underwater acoustic signal detection and transmission.

Acoustic emission spectrum characteristics of structural coal destruction in negative pressure environment

Abstract The uniaxial compression experiments under a low-pressure environment were performed by using structural coal samples. The frequency domain response characteristics of coal mass failure under loading in a low-pressure environment were acquired by FFT transformation and wavelet packet decomposition. The results show: As the loading stress of coal increases, the AE spectrum becomes more abundant, and the whole AE spectrum shows a left-shift trend. When the gas pressure increases, the acoustic emission signals change from low-frequency high-energy to high-frequency low-energy, the frequency band gradually narrates, and the spectrum changes from complex multi-peak shape to single-peak shape. As stress increases, the proportion of energy in the band 0-4.38 kHz gradually increases, while that in other bands gradually decreases. The energy response to stress changes in the two frequency bands of 2.92-4.38 kHz and 4.38-5.84 kHz is the most obvious. When the pressure changes, the energy in three frequency bands of 2.92-4.38 kHz, 4.38-5.84 kHz, and 7.3-8.76 kHz present an evident response trend with the pressure change, and the response trend (increase) of the latter two is exactly opposite (decrease) to that of the former. This phenomenon indicates that 2.92-4.38 kHz and 4.38-5.84 kHz are the characteristic frequency bands of the coal fracture process. The findings of this research offer crucial foundational data to support the monitoring and early warning of coal and gas outburst hazards.

Multifractal Characteristics of Acoustic Emission Signals of Concrete Under Axial Tension With Different Steel Fiber Contents

Multifractal Characteristics of Acoustic Emission Signals of Concrete Under Axial Tension With Different Steel Fiber Contents

Self correction technique for amplitude and phase errors of underwater acoustic arrays based on variable decibel Bayesian inference

Abstract Direction of arrival (DOA) estimation of underwater targets is an important research direction in the field of underwater acoustic array signal processing. Due to factors such as assembly errors and component aging, amplitude and phase errors between each channel of the array are common, resulting in distortion of the array flow pattern matrix. At the same time, the non-uniform distribution of noise intensity at the positions of each array element further increases the difficulty of DOA for underwater targets. Based on the characteristics of sparse spatial distribution of underwater targets, this paper uses the flexibility of hyperparametric modeling to construct an array observation signal graph model. On this basis, the effects of amplitude and phase errors and non-uniform noise on the array model are added, and a comprehensive observation model with clear distribution types of amplitude and phase errors, signals, and noise is established. The variational Bayesian inference theory is used to obtain closed solutions of various variables and parameters, so as to achieve joint estimation of target azimuth and error parameters. Simulation results and sea trial data processing results show that the algorithm can achieve amplitude and phase error self correction while completing DOA estimation in the absence of prior information on the number of sources.

Full-duplex two-way no-relay cross-media communication method based on acoustic wave conversion

Abstract Due to the difference in acoustic impedance between water and air, the water interface becomes a natural barrier. Underwater sound waves will bounce when they propagate to the water surface. Electromagnetic waves and light waves in the air will rapidly attenuate after passing through the water surface and going underwater, making it difficult to complete air-water cross-media information transmission. In this article, we first analyze the theoretical characteristics and vibration model of the water surface after medium collision, and develop a new full-duplex communication method across the air-ocean medium, which does not rely on relay equipment and only uses three transmission media in the air. Propagate signals in the sea channel physical field to achieve two-way communication, using the mutual conversion between laser, acoustic, and radio frequency signals to fill the gap in cross-media communication technology that can simultaneously achieve two-way, full-duplex and non-relay. It has been verified through experimental installations carried out in laboratory water tanks and large comprehensive anechoic pools that it is able to achieve full-duplex communication links across the air-sea surface, indicating that the combination of laser acousticization and radio frequency-acoustic wave conversion can be used across medium full-duplex wireless communication provides a feasible method for communication that breaks through the water interface.

Immediate auditory feedback regulates inter-articulator speech coordination in service to phonetic structure.

Research has shown that talkers reliably coordinate the timing of articulator movements across variation in production rate and syllable stress, and that this precision of inter-articulator timing instantiates phonetic structure in the resulting acoustic signal. We here tested the hypothesis that immediate auditory feedback helps regulate that consistent articulatory timing control. Talkers with normal hearing recorded 480 /tV#Cat/ utterances using electromagnetic articulography, with alternative V (/ɑ/-/ɛ/) and C (/t/-/d/), across variation in production rate (fast-normal) and stress (first syllable stressed-unstressed). Utterances were split between two listening conditions: unmasked and masked. To quantify the effect of immediate auditory feedback on the coordination between the jaw and tongue-tip, the timing of tongue-tip raising onset for C, relative to the jaw opening-closing cycle for V, was obtained in each listening condition. Across both listening conditions, any manipulation that shortened the jaw opening-closing cycle reduced the latency of tongue-tip movement onset, relative to the onset of jaw opening. Moreover, tongue-tip latencies were strongly affiliated with utterance type. During auditory masking, however, tongue-tip latencies were less strongly affiliated with utterance type, demonstrating that talkers use afferent auditory signals in real-time to regulate the precision of inter-articulator timing in service to phonetic structure.

A Radiological Clip Design Using Ultrasound Identification to Improve Localization.

We demonstrate the use of ultrasound to receive an acoustic signal transmitted from a radiological clip designed from a custom circuit. This signal encodes an identification number and is localized and identified wirelessly by the ultrasound imaging system. We designed and constructed the test platform with a Teensy 4.0 microcontroller core to detect ultrasonic imaging pulses received by a transducer embedded in a phantom, which acted as the radiological clip. Ultrasound identification (USID) signals were generated and transmitted as a result. The phantom and clip were imaged using an ultrasonic array (Philips L7-4) connected to a Verasonics™ Vantage 128 system operating in pulse inversion (PI) mode. Cross-correlations were performed to localize and identify the code sequences in the PI images. USID signals were detected and visualized on B-mode images of the phantoms with up to sub-millimeter localization accuracy. The average detection rate across 30,400 frames of ultrasound data was 98.1%. The USID clip produced identifiable, distinguishable, and localizable signals when imaged. Radiological clips are used to mark breast cancer being treated by neoadjuvant chemotherapy (NAC) via implant in or near treated lesions. As NAC progresses, available marking clips can lose visibility in ultrasound, the imaging modality of choice for monitoring NAC-treated lesions. By transmitting an active signal, more accurate and reliable ultrasound localization of these clips could be achieved and multiple clips with different ID values could be imaged in the same field of view.

Application of optical fiber distributed acoustic sensing system in ultrasonic detection

Abstract The optical fiber distributed acoustic sensing system can continuously detect acoustic signals along the optical fiber, with the characteristics of wide response frequency band, large capacity, anti-electromagnetic interference and so on. An ultra-sensitive distributed optical fiber acoustic sensing system (uDAS) was produced with a high response frequency up to 100 kHz and thousands of sensing points. The arrayed ultrasonic detection can be realized by the distributed characteristics of the system. For verifying its ultrasonic detection ability, it is applied to ultrasonic nondestructive testing of structural defects. 20 optical fiber ultrasonic detection units were constructed. Each one is a fiber ring with a diameter of about 1.5 cm and a length of 2 meters. Two cubic cement structures with a side length of 30 cm were manufactured for experiment, one of which had an artificial internal defect. The optical fiber array was attached to the surface of the cement structure, and the ultrasonic signal with a frequency of 40 kHz was excited at a single point on the opposite side. The ultrasonic propagation speed changes on account of the defects such as holes and loose texture in the propagation path. The defects in the structure are identified and located by analyzing the difference of arrival time of waves. It is expected that such sensing system could found important applications in structure defect detection.

Time Series Forecasting for Sparse Ring-shaped Array Photoacoustic Imaging Reconstruction

Abstract Photoacoustic computed tomography (PACT), which provides high optical absorption contrast and deep acoustic penetration, plays an important role in non-invasive biomedical imaging area. As the decrease of array elements, the reconstructed image suffers from severe artifacts. Recent studies utilize deep learning methods to improve the imaging quality of PACT based on image network design, but few were reported with raw data. To address this issue, this paper proposes a Wave to Wave Convolution Gate Recurrent-Net (WWCG-Net) to reconstruct photoacoustic image based on time series acoustic signal prediction. Simulation and experiment results show the superiority of our method compared with linear interpolation (LI) and eXtreme Gradient Boosting (XGBoost) in term of suppress artifact and improve resolution.