Acoustic Characteristics Research Articles

Cyclic compressive behavior of high-performance hybrid fiber reinforced recycled aggregate concrete: Degradation law and acoustic emission features

To tackle the issues of inadequate mechanical properties of recycled aggregate concrete and its limited application in load-bearing structural components, a high-performance hybrid fiber reinforced recycled aggregate concrete (HP-HFRAC) was developed in this study. Sixteen groups of HP-HFRAC specimens fabricated with varying replacement rates of recycled coarse aggregate and different fiber parameters were subjected to uniaxial compression, as well as acoustic emission (AE) tests, to investigate the cyclic behavior and uncover the underlying mechanisms. The findings demonstrated a strong correlation between the AE signals and the macroscopic phenomenon throughout the whole compression process. The AE amplitude distribution was found to be capable of identifying damage mechanisms, including matrix cracking (40–50 dB), fiber sliding (50–80 dB), and fiber pull-out (more than 80 dB). Additionally, the inclusion of hybrid fibers contributed to mitigating the degradation of stiffness and the accumulation of plastic strain, resulting in the ductile failure of the specimens and significantly enhancing the energy dissipation capacity. Specifically, the peak stress of specimens with 1.5% steel fibers increased by 19% compared to that of specimens without steel fibers, compensating for the strength loss due to the presence of recycled coarse aggregate. Finally, constitutive equations governing the skeleton curves and the unloading and reloading curves of HP-HFRAC subjected to cyclic compression were formulated, which were validated using both the test results and the predicted results of the physics-based data-driven model previously published in our companion paper. The findings of this study might contribute to the development of sustainable high performance recycled aggregate concrete for reliable application in load-bearing components.

Scleroglucan-Based Foam Incorporating Recycled Rigid Polyurethane Waste for Novel Insulation Material Production

This study details the synthesis and performance evaluation of a novel lightweight thermal and acoustic insulation material, resulting from the combination of a scleroglucan-based hydrogel and recycled rigid polyurethane waste powder. Through a sublimation-driven water-removal process, a porous three-dimensional network structure is formed, showcasing notable thermal and acoustic insulation properties. Experimental data are presented to highlight the material’s performance, including comparisons with commercially available mineral wool and polymeric foams. This material versatility is demonstrated through tunable mechanical, thermal and acoustic characteristics, achieved by strategically adjusting the concentration of the biopolymer and additives. This adaptability positions the material as a promising candidate for different insulation applications. Addressing environmental concerns related to rigid polyurethane waste disposal, the study contributes to the circular economy.

Experimental Study on the Effects of High- and Low-Temperature Cyclic Impact on Pore and Fracture Distributions and Acoustic Emission Characteristics of Coal Measure Sandstone

Experimental Study on the Effects of High- and Low-Temperature Cyclic Impact on Pore and Fracture Distributions and Acoustic Emission Characteristics of Coal Measure Sandstone

Characteristics of jet noise: A synthesis

It is a distinct privilege to produce this article to honor Professor Tam, the foremost authority on jet aeroacoustics. The fundamental characteristics of jet noise have been studied for 70 years, since the pioneering work of Lighthill in the 1950s. The acoustic analogy, with many variants, has served as the leading theory for nearly 50 years. Many leading researchers in the 1970s formulated theories to interpret the measured trends from subsonic and supersonic jets, using acoustic analogy and flow features as the framework. Quadrupoles, dipoles and monopoles were believed to constitute the sources of noise. The discovery of large-scale organized structures in free shear layers and jets sparked a different avenue of thinking about their importance for noise generation. Tam was the first to clearly demonstrate that these structures are efficient generators of noise and constitute the dominant noise sources, especially in the downstream direction. Now, two schools of thought emerged on the sources of jet noise. Experimental measurements showed that the mean flow as well as the turbulence statistics exhibit a self-similarity in the mixing layer and another similarity in the fully developed jet. Based on these observations, Tam proposed that since noise is generated by the turbulence of the jet, the noise spectra generated by fine-scale and large-scale turbulence should also exhibit self-similarity. By examining a large set of supersonic jet noise data acquired at NASA Langley, Tam offered evidence that the turbulent mixing noise of high-speed jets does consist of two independent self-similar components. In this paper, experimental evidence is compiled from an extensive database that quantifies the effect of several parameters that affect jet spectra. A new scaling method is developed and extended to noise predictions for realistic dual-stream nozzle geometries. The objectives of this paper are to serve as a synthesis of noise characteristics and to focus on application to real-world problems. Results from five different experimental measurements are examined: (1) farfield spectral characteristics; (2) azimuthal and polar correlations in the farfield; (3) correlations of jet turbulence fluctuations and farfield sound; (4) measurement of source distributions with an elliptic mirror; and (5) space-time correlation measurements in the nearfield with a cage array, and nearfield-farfield correlations. Two distinctly different trends are observed in the angular ranges of 50° – ∼120° and ∼120° – 165° for all the parameters investigated with the above five approaches. The salient observations are mutually supporting, and the cumulative weight lends credence to the proposition that there are two distinct sources of turbulent mixing noise.

Anthropogenic noise effects on Risso's dolphin vocalizations in the Gulf of Taranto (Northern Ionian sea, central Mediterranean sea)

Anthropogenic noise may significantly affect marine mammals' vocalizations. The aim of this work is to describe for the first time the acoustic features of the click trains, buzzes, burst pulses and whistles produced by the Risso's dolphins in the Gulf of Taranto (Northern Ionian Sea, Central Mediterranean), the concurrent levels of noise to which dolphins are exposed, and the potential reactive changes in the acoustic structure of the species vocalizations. About 6 h of recordings were collected from April 2019 to September 2022 during daytime boat surveys. Elevated ambient noise levels were mainly present at the frequencies lower than 63 Hz, at about 1 kHz and 50 kHz likely attributable to anthropogenic activity, such as shipping and sonars. Even if the acoustic features of all the vocalizations can be disrupted, especially with increasing noise below 1 kHz and between 20 and 63 kHz, the click trains were the most affected vocalizations, showing changes in their Inter-Click-Interval and in the Peak Amplitude. Since these vocalizations are used for navigation and searching for prey, their alteration raises concerns on the potential negative effects on the well-being of the Risso's dolphin population in the Gulf of Taranto.

Experimental study on the damage characteristics of cyclic disturbance and acoustic emission characteristics of different types of sandstones under high stress in deep mines

AbstractThree sandstone specimens common in rock engineering were selected to study the differences in the mechanical properties of rocks with different lithologies. The development and expansion of the internal cracks in the specimens were observed by combining the simulation system with the acoustic emission system. Through the combination of dynamic and static stresses, the deformation and damage of rocks under deep rock excavation and blasting were simulated. As the results show, the acoustic emission events of specimens with different lithologies under combined static and dynamic cyclic loading can be roughly divided into three phases: weakening, stabilizing, and surging periods. In addition, the acoustic emission characteristics of specimens with different lithologies show general consistency in different compression phases. The degree of fragmentation of specimens increases with the applied stress level; therefore, the stress level is one of the important factors influencing the damage pattern of specimens. The acoustic emission system was used to simulate the deformation and damage of rocks subjected to deep rock body excavation and engineering blasting. Cyclic dynamic perturbations under sinusoidal waves with a frequency of 5 Hz, a loading rate of 0.1 mm/min, a cyclic amplitude of 5 MPa, and a loading rate of 0.1 mm/min were applied to the three rock samples during the experiments. Among them, the fine‐grained sandstones are the most sensitive to the sinusoidal cyclic perturbation, followed by the muddy siltstone and the medium‐grained sandstones. On this basis, the acoustic emission energy release characteristics were analyzed, and the waveform characteristics in the damage evolution of the specimen under dynamic perturbation were studied by extracting the key points and searching for the main frequency eigenvalues.

AI-Empowered Multimodal Hierarchical Graph-Based Learning for Situation Awareness on Enhancing Disaster Responses

Situational awareness (SA) is crucial in disaster response, enhancing the understanding of the environment. Social media, with its extensive user base, offers valuable real-time information for such scenarios. Although SA systems excel in extracting disaster-related details from user-generated content, a common limitation in prior approaches is their emphasis on single-modal extraction rather than embracing multi-modalities. This paper proposed a multimodal hierarchical graph-based situational awareness (MHGSA) system for comprehensive disaster event classification. Specifically, the proposed multimodal hierarchical graph contains nodes representing different disaster events and the features of the event nodes are extracted from the corresponding images and acoustic features. The proposed feature extraction modules with multi-branches for vision and audio features provide hierarchical node features for disaster events of different granularities, aiming to build a coarse-granularity classification task to constrain the model and enhance fine-granularity classification. The relationships between different disaster events in multi-modalities are learned by graph convolutional neural networks to enhance the system’s ability to recognize disaster events, thus enabling the system to fuse complex features of vision and audio. Experimental results illustrate the effectiveness of the proposed visual and audio feature extraction modules in single-modal scenarios. Furthermore, the MHGSA successfully fuses visual and audio features, yielding promising results in disaster event classification tasks.

Multimodal Embodiment Research of Oral Music Traditions: Electromyography in Oud Performance and Education Research of Persian Art Music

With the recent advent of research focusing on the body’s significance in music, the integration of physiological sensors in the context of empirical methodologies for music has also gained momentum. Given the recognition of covert muscular activity as a strong indicator of musical intentionality and the previously ascertained link between physical effort and various musical aspects, electromyography (EMG)—signals representing muscle activity—has also experienced a noticeable surge. While EMG technologies appear to hold good promise for sensing, capturing, and interpreting the dynamic properties of movement in music, which are considered innately linked to artistic expressive power, they also come with certain challenges, misconceptions, and predispositions. The paper engages in a critical examination regarding the utilisation of muscle force values from EMG sensors as indicators of physical effort and musical activity, particularly focusing on (the intuitively expected link to) sound levels. For this, it resides upon empirical work, namely practical insights drawn from a case study of music performance (Persian instrumental music) in the context of a music class. The findings indicate that muscle force can be explained by a small set of (six) statistically significant acoustic and movement features, the latter captured by a state-of-the-art (full-body inertial) motion capture system. However, no straightforward link to sound levels is evident.

Improving Short-Duration Speaker Recognition by Joint Bark-Wavelet Acoustic Feature Coupling and Triplet Dual-Attention Mechanism Network

Improving Short-Duration Speaker Recognition by Joint Bark-Wavelet Acoustic Feature Coupling and Triplet Dual-Attention Mechanism Network

Performance evaluation of deep learning approaches for fault diagnosis of rotational mechanical systems using vibration, sound, and acoustic emission signals

The present study emphasizes an optimized deep learning algorithm for gearbox fault detection using vibration, sound, and acoustic emission signals. Statistical and acoustic features are extracted from these signals, and various neural network algorithms are explored. The supervised deep feed forward neural network (DFFNN) demonstrates excellent performance with vibration signals but limited accuracy with sound and acoustic emission signals. To address this, unsupervised algorithms are optimized and compared with vibration-based classification. The findings show that unsupervised neural networks, particularly the auto-encoder and stacked auto-encoder architectures, achieve improved classification accuracy by leveraging the unique characteristics of acoustic emission signals. The unsupervised models also effectively overcome the vanishing gradient problem via regularization, enhancing their training efficiency. The stacked auto-encoder, with multiple layers of encoders and decoders, reduces computation time by 40% and memory consumption. These optimized algorithms hold promise for automated fault detection systems. The auto-encoder and stacked auto-encoder, utilizing vibration, sound, and acoustic emission signals, offer enhanced classification accuracy and can facilitate real-time monitoring of rotating mechanical systems. However, further optimization is needed to maximize their performance. In a nutshell, the supervised DFFNN excels in utilizing vibration signals for fault detection, while the unsupervised models exploit the distinctive characteristics of acoustic emission signals. Future research will focus on refining these algorithms to enhance their effectiveness. Implementing these optimized deep learning approaches can lead to autonomous fault detection systems, eliminating the need for continuous human supervision.

Statistical relationships between surface form and sensory meanings of English words influence lexical processing.

Across spoken languages, there are some words whose acoustic features resemble the meanings of their referents by evoking perceptual imagery, i.e., they are iconic (e.g., in English, "splash" imitates the sound of an object hitting water). While these sound symbolic form-meaning relationships are well-studied, relatively little work has explored whether the sensory properties of English words also involve systematic (i.e., statistical) form-meaning mappings. We first test the prediction that surface form properties can predict sensory experience ratings for over 5,000 monosyllabic and disyllabic words (Juhasz & Yap, 2013), confirming they explain a significant proportion of variance. Next, we show that iconicity and sensory form typicality, a statistical measure of how well a word's form aligns with its sensory experience rating, are only weakly related to each other, indicating they are likely to be distinct constructs. To determine whether form typicality influences processing of sensory words, we conducted regression analyses using lexical decision, word recognition, naming and semantic decision tasks from behavioral megastudy data sets. Across the data sets, sensory form typicality was able to predict more variance in performance than sensory experience or iconicity ratings. Further, the effects of typicality were consistently inhibitory in comprehension (i.e., more typical forms were responded to more slowly and less accurately), whereas for production the effect was facilitatory. These findings are the first evidence that systematic form-meaning mappings in English sensory words influence their processing. We discuss how language processing models incorporating Bayesian prediction mechanisms might be able to account for form typicality in the lexicon. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prosodic features of polite speech

Prosodic features of polite speech

Mechanical properties and acoustic emission characteristics of soft rock with different water contents under dynamic disturbance

Uniaxial compression tests and cyclic loading acoustic emission tests were conducted on 20%, 40%, 60%, 80%, dry and saturated muddy sandstone by using a creep impact loading system to investigate the mechanical properties and acoustic emission characteristics of soft rocks with different water contents under dynamic disturbance. The mechanical properties and acoustic emission characteristics of muddy sandstones at different water contents were analysed. Results of experimental studies show that water is a key factor in the mechanical properties of rocks, softening them, increasing their porosity, reducing their brittleness and increasing their plasticity. Under uniaxial compression, the macroscopic damage characteristics of the muddy sandstone change from mono-bevel shear damage and ‘X’ type conjugate bevel shear damage to a roadway bottom-drum type damage as the water content increases. Dynamic perturbation has a strengthening effect on the mechanical properties of samples with 60% and less water content, and a weakening effect on samples with 80% and more water content, but the weakening effect is not obvious. Macroscopic damage characteristics of dry samples remain unchanged, water samples from shear damage and tensile–shear composite damage gradually transformed into cleavage damage, until saturation transformation monoclinic shear damage. The evolution of acoustic emission energy and event number is mainly divided into four stages: loading stage (I), dynamic loading stage (II), yield failure stage (III), and post-peak stage (IV), the acoustic emission characteristics of the stages were different for different water contents. The characteristic value of acoustic emission key point frequency gradually decreases, and the damage degree of the specimen increases, corresponding to low water content—high main frequency—low damage and high water content—low main frequency—high damage.

Dual-branch network with fused Mel features for logic-manipulated speech detection

In recent years, logic-manipulated speech detection has predominantly relied on single-feature and single-channel networks, which fail to consider a broader range of acoustic features. To address this limitation, we propose a novel approach based on a dual-branch network that incorporates fused Mel features for detecting logic-manipulated speech. The structure of our network consists of three main components. Firstly, we extract Mel spectrograms and Mel-frequency cepstral coefficients (MFCC) to address the deficiencies caused by the reduction in the information dimension. Then, to better utilize these two types of features, we construct a dual-branch network framework that enables the separate learning of temporal and frequency domain characteristics. Finally, at the backend of the network, we fuse the feature vectors for decision-making. Experimental evaluations conducted on the ASVspoof2019LA dataset demonstrate the effectiveness and superiority of our proposed method compared to state-of-the-art models and other acoustic features.

Non-Traditional Factors in Debussy's Creation Take the Prelude "Voiles" as an Example

"Voiles" stands as the second piece in Debussy's "Twenty-four Piano Preludes", marking a mature stage in his musical oeuvre. Debussy, as the pioneer of Impressionism style, demonstrates remarkable innovation and personality in contrast to traditional norms. Drawing from the acoustic expressiveness evident in Impressionist piano compositions, this study begins with an exploration of the aesthetic perception of acoustic color. It intertwines this exploration with the psychological concept of "synesthesia", which links time, space and visual perception. Focusing on auditory aesthetic perception of acoustic color, this paper takes “Voiles” as the research object, and analyzes its melody, structure and acoustic characteristics from the perspective of music analysis. In this way, the sound innovation and impressionism style in Debussy's creation can be explored.

Applicability of a new composite amusement ride safety device based on acoustic emission monitoring technology

Under the premise of ensuring safety, it is of great significance to realize the lightweight of the non-main load-bearing parts of amusement facilities. The purpose of this study is to study the failure process of a newly designed carbon fiber bumper by using acoustic emission technology. First of all, the design of carbon fiber anti-collision bar can effectively restrain passengers, and its weight is reduced by nearly two-thirds compared to traditional metal materials. Subsequently, the load-bearing capacity of the bumper was tested and acoustic emission monitoring was carried out. The test results show that this new type of combined structure of amusement facility has high reliability, which exceeds the safety factor of 3.5 required by the steel structure of amusement facility. In addition, Renyi entropy was used to select the best window function of short-time Fourier transform, and the frequency domain characteristics of acoustic emission signals of typical damage modes were discussed through appropriately selected windows function. A classifier based on supervised machine learning is established by combining frequency features and acoustic emission feature parameters. Furthermore, the use of classifiers helps to understand the damage behaviour of composite structures.

Theoretical and numerical study on transient acoustic wave propagation across ice layers in the Arctic Ocean.

The study of transient acoustic wave propagation across the Arctic Ocean ice layer provides theoretical guidance for the design of trans-ice acoustic communication systems. In this study, the Arctic Ocean was modeled as an ice-water composite structure, where the ice and water are regarded as an elastic solid and liquid, respectively. An analytical transient solution for acoustic wave propagation in this structure was derived using the eigenfunction expansion method. Further, the numerical procedures were presented and used to analyze the acoustic wave propagation characteristics across the ice layer. The results show that waveforms corresponding to the radial displacements are more severely distorted than the axial displacements. The amplitudes of the radial and axial displacements decreased rapidly with increasing propagation distance. The ice thickness had a greater impact on the radial displacement than axial displacement; the thicker the ice, the greater the distortion for both radial and axial displacements.

Method for vibration and acoustic radiation analysis of underwater stiffened plates with acoustic coatings

Based on the effective medium theory as well as the three-dimensional viscoelastic theory, a semi-analytical model of the three-dimensional underwater vibration and acoustic radiation with cavity-type stiffened plate structures covered with acoustic coatings is established to solve the issues related to underwater vibration and acoustic radiation calculation of the stiffened plate and shell structure with complex acoustic coatings. By comparing the far-field acoustic radiation characteristics of the proposed semi-analytical model and the finite element numerical model, this research verifies the validity and accuracy of the proposed model. On such a basis, this research preliminarily investigates the coupling effect exerted by stiffeners and parent plates in stiffened plates as well as the vibroacoustic characteristics of acoustic coatings, thereby providing a valuable idea for the prediction of vibroacoustic characteristics concerning underwater structures.

Research on numerical simulation of tandem thrusters flow-induced noise

A tandem propeller is a kind of propulsion device that is installed on the outer axis and the inner axis with the same axis respectively and rotates positively and negatively. Taking DTMB tandem thrusters as an example, which consists of propeller3689 and propeller3849, the hydrodynamic characteristic simulation of the tandem thrusters is completed, using sound analogy theory. The simulation results are consistent with the experimental results. The speed distribution of the tandem thrusters and the hydrodynamic wake of the tandem thrusters are analyzed. Through the analysis of acoustic radiation characteristics, it can be concluded that the noise characteristics of tandem thrusters are more complex than those of ordinary propellers, and there are more line spectrum characteristics in them.

Zero group velocity feature in CFRP-Nomex honeycomb structure and its use for debonding detection

Carbon fiber reinforced plastic (CFRP) − Nomex adhesive honeycomb structures are widely used in aerospace due to their excellent properties. However, debonding defects pose a significant challenge to structural safety due to their hidden nature and high risk. In this work, to address the debonding detection in the CFRP-Nomex honeycomb structure, a method based on the zero group velocity (ZGV) feature is proposed. By calculating the dispersion curve of the structure, it is found that there is the ZGV on the S1 branch. Further analysis revealed that this ZGV feature is generated by the repulsion between the S2 and S1 branches. The simulation and experimental analysis of the acoustic propagation characteristics in the honeycomb structure confirmed the presence of the ZGV feature with its energy localized near the exciting point. When debonding defects occur in the honeycomb structure, this feature disappears. Therefore, the ZGV feature can be used for the debonding detection of CFRP-Nomex honeycomb structures. Compared with ultrasonic C-scan, the results demonstrate that the proposed method can accurately detect and locate debonding defects with a diameter of 20 mm. Due to its simplicity, the method does not require sophisticated equipment or complex signal processing procedures, making it suitable for in-service inspection.