Acoustic Signals Research Articles

Effects of Speech Characteristics on Electroglottographic and Instrumental Acoustic Voice Analysis Metrics in Women With Structural Dysphonia Before and After Treatment.

Literature suggests a dependency of the acoustic metrics, smoothed cepstral peak prominence (CPPS) and harmonics-to-noise ratio (HNR), on human voice loudness and fundamental frequency (F0). Even though this has been explained with different oscillatory patterns of the vocal folds, so far, it has not been specifically investigated. In the present work, the influence of three elicitation levels, calibrated sound pressure level (SPL), F0 and vowel on the electroglottographic (EGG) and time-differentiated EGG (dEGG) metrics hybrid open quotient (OQ), dEGG OQ and peak dEGG, as well as on the acoustic metrics CPPS and HNR, was examined, and their suitability for voice assessment was evaluated. In a retrospective study, 29 women with a mean age of 25 years (± 8.9, range: 18-53) diagnosed with structural vocal fold pathologies were examined before and after voice therapy or phonosurgery. Both acoustic and EGG signals were recorded simultaneously during the phonation of the sustained vowels /ɑ/, /i/, and /u/ at three elicited levels of loudness (soft/comfortable/loud) and unconstrained F0 conditions. A linear mixed-model analysis showed a significant effect of elicitation effort levels on peak dEGG, HNR, and CPPS (all p < .01). Calibrated SPL significantly influenced HNR and CPPS (both p < .01). Furthermore, F0 had a significant effect on peak dEGG and CPPS (p < .0001). All metrics showed significant changes with regard to vowel (all p < .05). However, the treatment had no effect on the examined metrics, regardless of the treatment type (surgery vs. voice therapy). The value of the investigated metrics for voice assessment purposes when sampled without sufficient control of SPL and F0 is limited, in that they are significantly influenced by the phonatory context, be it speech or elicited sustained vowels. Future studies should explore the diagnostic value of new data collation approaches such as voice mapping, which take SPL and F0 effects into account.

A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

To address the challenges in processing and identifying mine acoustic emission signals, as well as the inefficiency and inaccuracy issues prevalent in existing methods, an enhanced CELMD approach is adopted for preprocessing the acoustic emission signals. This method leverages correlation coefficient filtering to extract the primary components, followed by classification and recognition using the Swin Transformer neural network. The results demonstrate that the improved CELMD method effectively extracts the main features of the acoustic emission signals with higher decomposition accuracy and reduced occurrences of mode mixing and end effects. Furthermore, the Swin Transformer neural network exhibits outstanding performance in classifying acoustic emission signals, surpassing both convolutional neural networks and ViT neural networks in terms of accuracy and convergence speed. Moreover, utilizing preprocessed data from the improved CELMD enhances the performance of the Swin Transformer neural network. With an increase in data volume, the accuracy, stability, and convergence speed of the Swin Transformer neural network continuously improve, and using preprocessed data from the enhanced CELMD yields superior training results compared to those obtained without preprocessing.

Acoustically Enhanced Triboelectric Stethoscope for Ultrasensitive Cardiac Sounds Sensing and Disease Diagnosis.

Electronic stethoscope used to detect cardiac sounds that contains essential clinical information is a primary tool for diagnosis of various cardiac disorders. However, the linear electro-mechanical constitutive relation makes conventional piezoelectric sensors rather ineffective to detect low-intensity, low-frequency heart acoustic signal without the assistance of complex filtering and amplification circuits. Herein, we find that triboelectric sensor features superior advantages over piezoelectric one for micro-quantity sensing originated from the fast saturated constitutive characteristic. As a result, the triboelectric sensor shows ultrahigh sensitivity (1215mV/Pa) than the piezoelectric counterpart (21mV/Pa) in the sound pressure range of 50 - 80dB under the same testing condition. By designing a trumpet-shaped auscultatory cavity with a power function cross-section to achieve acoustic energy converging and impedance matching, triboelectric stethoscope delivers 36dB signal-to-noise ratio for human test (2.3 times of that for piezoelectric one). Further combining with machine learning, five cardiac states can be diagnosed at 97% accuracy. In general, the triboelectric sensor is distinctly unique in basic mechanism, provides a novel design concept for sensing micromechanical quantities, and presents significant potential for application in cardiac sounds sensing and disease diagnosis. This article is protected by copyright. All rights reserved.

Experimental Study on Near-Wall Laser-Induced Cavitation Bubble Micro-Dimple Formation on 7050 Aluminum Alloy

To investigate the feasibility and formation laws of fabricating micro-dimples induced by near-wall laser-induced cavitation bubble (LICB) on 7050 aluminum alloy. A high-speed camera and a fiber-optic hydrophone system were used to capture pulsation evolution images and acoustic signals of LICB. Meanwhile, a three-dimensional profilometer was employed to examine the contour morphology of the surface micro-dimple on the specimen. The results show that at an energy level of 500 mJ, the total pulsation period for the empty bubble is 795 μs, with individual pulsation periods of 412.5 μs, 217 μs, and 165 μs for the first, second, and third cycles, respectively, with most energy of the laser and bubble being consumed during the first evolution period. Under the synergy of the plasma shock wave and collapse shock wave, a spherical dimple with a diameter of 450 μm is formed on the sample surface with copper foil as the absorption layer. A model of micro-dimple formed by LICB impact is established. As the energy increases, the depth of the surface micro-dimple peaks at an energy of 400 mJ and then decreases. The depth of the surface micro-dimple increases with the increase in the number of impacts; the optimal technology parameters for the micro-dimple formation by LICB impact are as follows: the absorption layer is copper foil, the energy is 400 mJ, and the number of impacts is three.

Production of sounds by squirrelfish during symbiotic relationships with cleaner wrasses

Examples of symbiotic relationships often include cleaning mutualisms, typically involving interactions between cleaner fish and other fish, called the clients. While these cleaners can cooperate by removing ectoparasites from their clients, they can also deceive by feeding on client mucus, a behavior usually referred to as “cheating behavior” that often leads to a discernible jolt from the client fish. Despite extensive studies of these interactions, most research has focused on the visual aspects of the communication. In this study, we aimed to explore the role of acoustic communication in the mutualistic relationship between cleaner fishes and nine holocentrid client species across four regions of the Indo-Pacific Ocean: French Polynesia, Guam, Seychelles, and the Philippines. Video cameras coupled with hydrophones were positioned at various locations on reefs housing Holocentridae fish to observe their acoustic behaviors during interactions. Our results indicate that all nine species of holocentrids can use acoustic signals to communicate to cleaner fish their refusal of the symbiotic interaction or their desire to terminate the cooperation. These sounds were predominantly observed during agonistic behavior and seem to support visual cues from the client. This study provides a novel example of acoustic communication during a symbiotic relationship in teleosts. Interestingly, these vocalizations often lacked a distinct pattern or structure. This contrasts with numerous other interspecific communication systems where clear and distinguishable signals are essential. This absence of a clear acoustic pattern may be because they are used in interspecific interactions to support visual behavior with no selective pressure for developing specific calls required in conspecific recognition. The different sound types produced could also be correlated with the severity of the client response. There is a need for further research into the effects of acoustic behaviors on the quality and dynamics of these mutualistic interactions.

Underwater Non-stationary Acoustic Signal Detection Based on the STHOC Noise Suppression

Underwater Non-stationary Acoustic Signal Detection Based on the STHOC Noise Suppression

Lipwatch: Enabling Silent Speech Recognition on Smartwatches using Acoustic Sensing

Silent Speech Interfaces (SSI) on mobile devices offer a privacy-friendly alternative to conventional voice input methods. Previous research has primarily focused on smartphones. In this paper, we introduce Lipwatch, a novel system that utilizes acoustic sensing techniques to enable SSI on smartwatches. Lipwatch leverages the inaudible waves emitted by the watch's speaker to capture lip movements and then analyzes the echo to enable SSI. In contrast to acoustic sensing-based SSI on smartphones, our development of Lipwatch takes into full consideration the specific scenarios and requirements associated with smartwatches. Firstly, we elaborate a wake-up-free mechanism, allowing users to interact without the need for a wake-up phrase or button presses. The mechanism utilizes the inertial sensors on the smartwatch to detect gestures, in combination with acoustic signals that detecting lip movements to determine whether SSI should be activated. Secondly, we design a flexible silent speech recognition mechanism that explores limited vocabulary recognition to comprehend a broader range of user commands, even those not present in the training dataset, relieving users from strict adherence to predefined commands. We evaluate Lipwatch on 15 individuals using a set of the 80 most common interaction commands on smartwatches. The system achieves a Word Error Rate (WER) of 13.7% in user-independent test. Even when users utter commands containing words absent in the training set, Lipwatch still demonstrates a remarkable 88.7% top-3 accuracy. We implement a real-time version of Lipwatch on a commercial smartwatch. The user study shows that Lipwatch can be a practical and promising option to enable SSI on smartwatches.

A pipeline corrosion detecting method using percussion and residual neural network

Corrosion of pipeline walls can lead to serious safety accidents such as leaks, fires and even explosions. This paper proposes a corrosion detection method using deep learning based on percussion sound for pipelines. The percussion induced acoustic signals are processed by wavelet threshold noise reduction and double threshold endpoint detection to generate the Mel spectrograms, and then an 18-layer residual network (ResNet18) is used to mine the depth information and classify the degree of pipeline corrosion. We conducted experiments to verify the validity of the approach. Seven working conditions are generated by electrochemical corrosion of a pipe specimen, and percussions are applied at five different positions under the same working conditions to collect the impact acoustic signals. The test results show that the method can quickly, efficiently and accurately detect the degree of pipeline corrosion, classify the degree of pipe corrosion without being affected by the striking position Therefore, the model has great potential for application in detecting the internal corrosion of pipelines based on percussion sounds.

Pushing the Limits of Acoustic Spatial Perception via Incident Angle Encoding

With the growing popularity of smart speakers, numerous novel acoustic sensing applications have been proposed for low-frequency human speech and high-frequency inaudible sounds. Spatial information plays a crucial role in these acoustic applications, enabling various location-based services. However, typically commercial microphone arrays face limitations in spatial perception of inaudible sounds due to their sparse array geometries optimized for low-frequency speech. In this paper, we introduce MetaAng, a system designed to augment microphone arrays by enabling wideband spatial perception across both speech signals and inaudible sounds by leveraging the spatial encoding capabilities of acoustic metasurfaces. Our design is grounded in the fact that, while sensitive to high-frequency signals, acoustic metasurfaces are almost non-responsive to low-frequency speech due to significant wavelength discrepancy. This observation allows us to integrate acoustic metasurfaces with sparse array geometry, simultaneously enhancing the spatial perception of high-frequency and low-frequency acoustic signals. To achieve this, we first utilize acoustic metasurfaces and a configuration optimization algorithm to encode the unique features for each incident angle. Then, we propose an unrolling soft thresholding network that employs neural-enhanced priors and compressive sensing for high-accuracy, high-resolution multi-source angle estimation. We implement a prototype, and experimental results demonstrate that MetaAng maintains robustness across various scenarios, facilitating multiple applications, including localization and tracking.

Research on a Corrosion Detection Method for Oil Tank Bottoms Based on Acoustic Emission Technology

This paper presents an acoustic emission (AE) detection method for refined oil storage tanks which is aimed towards specialized places such as oil storage tanks with high explosion-proof requirements, such as cave oil tanks and buried oil tanks. The method utilizes an explosion-proof acoustic emission instrument to detect the floor of a refined oil storage tank. By calculating the time difference between the defective acoustic signal and the speed of acoustic wave transmission, a mathematical model is constructed to analyze the detected signals. An independent channel AE detection system is designed, which can store the collected data in a piece of independent explosion-proof equipment, and can analyze and process the data in a safe area after the detection, solving the problems of a short signal acquisition distance and the weak safety protection applied to traditional AE instruments. A location analysis of the AE sources is conducted on the bottom plate of the tank, evaluating its corrosion condition accurately. The consistency between the evaluation and subsequent open-tank tests confirms that using AE technology effectively captures corrosion signals from oil storage tanks’ bottoms. The feasibility of carrying out online inspection under the condition of oil storage in vertical steel oil tanks was verified through a comparison with open inspections, which provided a guide for determining the inspection target and opening order of large-scale oil tanks.

Afitness: Fitness Monitoring on Smart Devices via Acoustic Motion Images

Recently, as fitness has become a popular part of people’s lives, the intention to record fitness processes and assess the standards of fitness movements has grown increasingly keen. However, the existing approaches have some limitations, for example, wearable devices can hinder users’ fitness activities; computer vision–based solutions pose the risk of privacy breach, and so on. Fortunately, we observed that smartspeaker, acoustic-based sensing is a promising method of activity monitoring. In this article, we propose Afitness, an acoustic-based sensing system that enables non-intrusive, passive, and high-precision fitness detection. Afitness has the following three innovations. (i) We utilize pulse compression to generate high-precision motion distance images on commercial devices that can be visually recognized. (ii) We propose a data augmentation algorithm, which also incorporates transfer learning to greatly reduce the pressure of data collection. (iii) We exploit incremental learning techniques that allow Afitness to improve the portability of our system and recognize new actions. Overall, Afitness achieves acoustic signal interpretability and environmental reliability detection.

Acoustic logging array signal denoising using U-net and a case study in TangGu oil field

Abstract This study developed a noise-reduction method for acoustic logging array signals using a deep neural network algorithm in the time-frequency domain. Initially, we derived analytical solutions for the received waveforms when the acoustic logging tool was positioned either at the centre or eccentrically within the borehole. To simulate the received waveforms across various formations, we developed a real-axis integration algorithm. Subsequently, we devised a noise-reduction algorithm workflow based on a convolutional neural network (CNN) and configured the structure and parameters of the U-net using TensorFlow. To address the scarcity of open datasets, we established both signal and noise datasets. The signal dataset was generated using theoretical simulation encompassing various model parameters, while the noise dataset was collected during tool testing and downhole operations. The trained model demonstrated substantial noise-reduction capabilities during validation. To validate the effectiveness of the algorithm, we applied noise reduction to actual data collected during downhole operations in the TangGu oilfield, yielding impressive results across different types of noisy data. Therefore, the U-net-based time-domain noise-reduction algorithm proposed in this paper holds the potential to significantly improve the quality of acoustic logging array signals.

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

Abstract Inelastic x-ray scattering (IXS) experiments on liquid sulphur were carried out below (140◦C) and above (180◦C) the polymerisation temperature Tλ 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 τµis 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 τα of the memory function. The τα 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 thesmaller than the minimum values of macroscopic shear viscosity, in⃗dicating that largeQ 0 limit are much changes in relaxation dynamics are expected with Q in the GHz and MHz excitation regimes.&amp;#xD;

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

Abstract Discharge plasma parameter measurement is a key focus in low-temperature plasma research. Traditional diagnostics often require costly equipment, whereas electro-acoustic signals provide a rich, non-invasive, and less complex source of discharge information. This study harnesses machine learning to decode these signals. It establishes links between electro-acoustic signals and gas discharge parameters, such as power and distance, thus streamlining the prediction process. By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques, the Mel-Frequency Cepstral Coefficients (MFCCs) of the acoustic signals are extracted to construct the predictors. Three machine learning models (Linear Regression, k-Nearest Neighbors, and Random Forest) are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance. All models display impressive performance in prediction precision and fitting abilities. Among them, the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error (MSE=0.00571) and the highest R-squared value (R^2=0.93877). The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm, which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.

Detection of Gate Valve Leaks through the Analysis Fractal Characteristics of Acoustic Signal

This paper considers the possibility of using monofractal and multifractal analysis of acoustic signals to detect water leaks through gate valves. Detrended fluctuation analysis (DFA) and multifractal detrended fluctuation analysis (MF-DFA) were used. Experimental studies were conducted on a ½-inch nominal diameter wedge valve, which was fitted to a ¾-inch nominal diameter steel pipeline. The water leak was simulated by opening the valve. The resulting leakage rates for different valve opening conditions were 5.3, 10.5, 14, 16.8, and 20 L per minute (L/min). The Hurst exponent for acoustic signals in a hermetically sealed valve is at the same level as a deterministic signal, while the width of the multifractal spectrum closely matches that of a monofractal process. When a leak occurs, turbulent flow pulsations appear, and with small leak sizes, the acoustic signals become anticorrelated with a high degree of multifractality. As the leakage increases, the Hurst exponent also increases and the width of the multifractal spectrum decreases. The main contributor to the multifractal structure of leak signals is small, noise-like fluctuations. The analysis of acoustic signals using the DFA and MF-DFA methods enables determining the extent of water leakage through a non-sealed gate valve. The results of the experimental studies are in agreement with the numerical simulations. Using the Ansys Fluent software (v. 19.2), the frequencies of flow vortices at different positions of gate valve were calculated. The k-ω SST turbulence model was employed for calculations. The calculations were conducted in a transient formulation of the problem. It was found that as the leakage decreases, the areas with a higher turbulence eddy frequency increase. An increase in the frequency of turbulent fluctuations leads to enhanced energy dissipation. Some of the energy from ordered processes is converted into the energy of disordered processes.

Weak abnormal acoustic signal enhancement and recognition using squeeze-and-excitation attention based denoising convolutional neural network during high-dam flood discharging

Acoustic signals (particularly cavitation acoustic signals) generated during the flood discharge of high dams are highly sensitive to various abnormal situations, whereas weak abnormal signal recognition under strong discharge-noise interference is extremely challenging. Based on the prototype and model experiments, the related abnormal acoustic signals and discharge noise were recorded to construct datasets. Subsequently, using the framework of the deep neural network (DNN) speech enhancement method, a squeeze-and-excitation attention based denoising convolutional neural network (DnCNN) based method for weak abnormal acoustic signal enhancement and recognition was proposed and verified using two case studies of cavitation acoustic signal enhancement and multicategory acoustic signal enhancement and recognition. Compared with the DnCNN method and traditional signal processing methods (such as wavelet, empirical mode decomposition, least mean square, and recursive least square), the proposed method achieved excellent signal enhancement performance after training based on limited prior knowledge of signal and noise. It also demonstrated good generalization ability and robustness in multicategory tasks, which significantly improved the abnormal signal recognition accuracy. This study provides technical support for the practical application of acoustic monitoring based on DNN for safety during the flood discharge of high dams.

Acoustic emission signatures for quantifying damage patterns in half grouted sleeve connection under tensile load

The half-grouted sleeve connection (HGSC) have been widely used as connection tools for prefabricated concrete (PC) buildings. It is crucial to maintain the safety and stability of the nodes and detecting internal defects in the grouting sleeve for the safety of the entire structure. Therefore, understanding the mechanical behavior and damage mechanism of HGSC is essential for the design and construction of PC. Currently, there are few on-site techniques applicable for monitoring the damage progress. The acoustic emission (AE) as a passive method shows great potential to characterize the damage in the HGSC. Therefore, in this paper, we propose to use AE to quantify the damage of the HGSC with defects under the tensile load. The artificial defects were introduced into HGSC with different defective rates. The monotonic tensile load was applied to each HGSC sample with a loading rate of 0.33 mm/s and tested to the failure. In this process, the AE signals were collected by AE transducers. For the classification and prediction of damage signals, we utilized a machine learning framework tailored for HGSC. This framework integrates unsupervised k-means++ cluster analysis and supervised K-Nearest Neighbors (K-NN) for damage classification and prediction. The experimental results showed that four AE signal clusters can be defined during the progress of tensile failure:(i) microcrack expansion;(ii) the friction signals of rebar and high-strength grouts; (iii)the generation of microcracks; (iv) plastic deformation of rebar. The corresponding AE signatures were identified. The K-NN model trained on the training data provides a high accuracy in predicting different class of defects. It was concluded that the proposed machine learning framework is a promising and powerful tool for realizing the on-site damage detection and prediction for HGSC.

Features of heat/mass transfer and explosive water boiling at the laser fiber tip

Experimentally, as well as using numerical simulation methods, the features of the processes of heat and mass transfer and explosive boiling of water, initiated by continuous laser radiation with λ = 1.94 μm, emerging from the tip of the laser fiber, are studied. The trajectories of the microvolume with the maximum temperature of the superheated liquid under laser heating, in which the probability of explosive boiling up is maximum, are determined. The calculations took into account changes in the density of superheated water and the dependence of the absorption coefficient of laser radiation on temperature. Using acoustic methods and high-speed video recording, it is shown that during explosive boiling up, the duration of the leading front of the acoustic signal is ∼300 ns. Such a long duration of pressure rise at the receiving point is explained by the fact that explosive boiling occurs in a wide area of superheated liquid near fiber tip, when the nuclei located in it are stimulated by an initially arising pressure pulse.

Experimental Study of Laser-Induced Cavitation Bubbles near Wall: Plasma Shielding Observation

To investigate the plasma shielding of laser-induced cavitation bubbles near a wall, a pulsed laser with different energies was selected to induce cavitation bubbles on the surface of 7050-T7451 aluminum alloy. A high-speed camera captured the evolution of the cavitation bubble, while a fiber-optic hydrophone system collected the acoustic signals during the evolution. Finally, a confocal microscope was used to view and analyze the surface morphology of 7050 aluminum alloy. The experimental results indicate that as the laser energy increases, the diameter, the evolution time, the pressure of the bubble, and both the pit diameter and depth all increase. Beyond an energy level of 1.4 J, the maximum diameter and the evolution time of the laser-induced cavitation bubble begin to decrease; the maximum diameter decreases by 2.04%, and the first evolution time decreases by 3.26%. Plasma shielding was observed in this experiment. Considering that the essence of a laser-induced cavitation bubble is the interaction between a high-energy laser and a liquid medium, the abnormal decrease in the maximum diameter, evolution time, and sound pressure epitomizes the manifestation of plasma shielding.

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.