Characteristics Of Acoustic Signals Research Articles

Experimental study on the evolutionary characteristics of acoustic signals produced by granite damage under uniaxial compression

Acoustic signals emitted during rock fracture constitute an important tool for rock damage evaluation. To investigate the evolutionary characteristics of acoustic emission (AE), microseismic (MS), and sound signals produced by hard rock fracture, uniaxial compression tests on granite specimens observed by AE, MS and sound monitoring were carried out. The evolution characteristics of the acoustic signal index, including its waveform, fractal dimension, b value, main frequency, energy proportion of signal frequency bands, and signal activeness, were analysed. The results indicate that there are significant differences in some characteristics of the AE, MS, and sound on the eve of granite failures, such as the waveform amplitude density, the average decline rate of the b value, the distribution of the main frequency, and the evolution of the energy proportion of the advantage frequency band. The three types of acoustic signals can characterize different scales of rock fracture under uniaxial compression. AE is sensitive to small-scale rock fractures, and MS and sound are sensitive to large-scale rock fractures. In addition, a unified damage evolution equation established by acoustic signals is proposed to quantitatively describe the damage process of granite specimens during uniaxial compression tests.

Why we talk about voices as we do

The problem of how to characterize voice quality is an endless source of debate and frustration across disciplines. The richness of the vocabulary available to describe voice is overwhelming, but the density of the information conveyed by voice has led some scholars to conclude that language can never adequately specify what we hear. Others have argued that terminology derives from tradition and lacks an empirical basis, so that language-based scales are inadequate a priori. Finally, efforts to link terms to acoustic signal characteristics have had limited success. However, a reconsideration suggests that a few terms appear consistently across studies, disciplines, and eras. These terms align with dimensions that account for acoustic variance in voice across speakers, regardless of gender, language spoken, or the kind of speech sample, and correlate with physical size and arousal across many species. They, thus, may have an evolutionary basis. This suggests talk about voices rests on a bedrock of biology: We have evolved to perceive voices in terms of size/arousal, and these factors structure both voice acoustics and the language we use to describe voices. Such linkages could help integrate studies of physical signals and their meaning, producing a truly interdisciplinary approach to voice.

Study on characteristics of acoustic signals generated by different DC discharge modes

Acoustic signals contain rich discharge information. In this study, the acoustic signal characteristics of transient glow, spark, and glow discharges generated through DC pin–pin discharge were investigated. The signals were analyzed in the time, frequency, and time–frequency domains, and the correlation between the electric and the acoustic signal was studied statistically. The results show that glow discharge does not produce measurable sound signals. For the other modes, with a decrease in the discharge gap, the amplitude of the acoustic signal increases sharply with mode transformation, the short-time average energy becomes higher, and the frequency components are more abundant. Meanwhile, the current pulse and sound pressure pulse have a one-to-one relationship in the transient glow and spark regimes, and they are positively correlated in amplitude. A brief theoretical analysis of the mechanism of plasma sound and the trends of signals in different modes is presented. Essentially, the change in the discharge energy is closely related to the sound generation of the plasma.

FREQUENCY CHARACTERISTICS OF ACOUSTIC FEATURES OF SOUND SIGNALS IN THE LUNGS OF CHILDREN WITH PNEUMONIA USING A NEW ACOUSTIC DIAGNOSTIC DEVICE "TREMBITA-CORONA"

Relevance. Community-acquired pneumonia (CAP) is an urgent problem in practical medicine and the main cause of morbidity and mortality in the world. Diagnosing pneumonia is complex and requires a comprehensive study, taking into account many factors. The development of passive diagnostic methods, such as acoustic ones, is especially promising for children, as this approach reduces significantly the level of radiation exposure to an unformed child's body.The aim. To determine the peculiarities of the frequency characteristics of acoustic signals in the lungs of children with pneumonia using the new acoustic diagnostic device «Trembita-Corona».Materials and methods. We studied 120 children aged 1 month to 18 years, who were treated in the pediatric departments of the Children's Clinical Hospital №. 5 and Children's Clinical Hospital № 7. The children were divided into two groups: Group I – 60 patients with community-acquired pneumonia, Group II – 60 healthy children. Children from І group underwent a comprehensive examination. All children underwent research using the "Trembita-Corona" acoustic monitoring device for the diagnosis of respiratory noise and localization of lung lesions. The computerized analysis of breathing sounds was carried out using mathematical methods without involving human factors.The study was conducted in accordance with the international principles of conducting clinical studies GCP, GLP, the protocol was approved at the meeting of the Commission on Bioethical Expertise at the National Medical University named after O.O. Bogomolets (protocol No. 138 of November 10, 2020). Informed consent of parents/guardians was obtained for conducting the study, which was approved at the same meeting of the Commission on Bioethical Expertise at Bogomolets National Medical University (protocol № 138 of November 10, 2020).Statistical processing of the obtained results was carried out using the licensed programs "MedStat", "Matlab" and with the original calculation programs included in the "Trembita-Corona" system. This work is a part of the complex research work (CRW) of the Department of Pediatrics of Postgraduate Education of Bogomolets National Medical University "Characteristics of the state of health of children who have suffered from COVID-19 and the justification of medical and rehabilitation measures" (2022-2024). State registration number 0122U000486. The authors are performers of the CRW fragment.Results. In children, the clinical picture of CAP consisted of pulmonary (respiratory) complaints, symptoms of intoxication, RF, and local physical changes. Respiratory lung sounds in the frequency range from 0.1 Hz to 30 kHz were studied in all patients using the "Trembita-Corona" device and the corresponding original software. The most promising is the selection of the pathological process of lung damage in pneumonia in the ranges of 0, 1, 2, 3, 4, 5, 6 octaves based on the differences between the average signal power (p<0.05) in children with САP and healthy children.Conclusion. We, in cooperation with leading specialists of the National Aviation University, have developed an experimental sample of the "Trembita-Corona" acoustic monitoring device for the diagnosis of respiratory lung sounds. The "Trembita-Corona" acoustic monitoring device is a new and promising acoustic method for determining the location of a pathological process in the lungs. It was found that the most promising is the selection of the pathological process of lung damage in pneumonia in the ranges of 0, 1, 2, 3, 4, 5, 6 octaves based on the differences between the average signal power (p<0.05) in children with САP and healthy children.

Characteristics of Nanosecond Laser-Induced Underwater Acoustic Signals Across the Water–Air Interface

Characteristics of Nanosecond Laser-Induced Underwater Acoustic Signals Across the Water–Air Interface

Acoustic and temperature signals generated by subterranean termite infestation: its characteristics and implementations

The innovation in the proposed termite detection system can detect and predict the population non-destructively based on acoustic and temperature signals. This study aims to identify the characteristics of acoustic and temperature signals of subterranean termite infestation. Various termite populations were inserted into the pine board for 21 days as a feeding test, then infestation was monitored through the continuous acoustic-temperature signals response after 24 h. Based on the signal analysis, the peak of daily termite activity can be found in the afternoon, while the evening until early morning contained significantly less activity. This fluctuation was significantly associated with ambient temperature, the higher the temperature, the greater the activity. Furthermore, the signal analysis revealed that the higher the termite population, the warmer the board temperature with an average of 0.101°C. Based on the results, extensive knowledge is essential to effectively develop the signal processing package in the termite detection system.

A Deep Learning Approach for Identifying and Discriminating Spoken Arabic Among Other Languages

Spoken Language Identification (SLID) is an important step in speech-to-speech translation systems and multi-lingual automatic speech recognition. In recent research, deep learning mechanisms have been the prevailing approaches for spoken language identification. This paper aims to study, detect, and analyze spoken languages similar to Arabic in pronouncing certain words and then proposes a deep learning-based architecture, specifically the Bidirectional Long Short Term Memory (BLSTM), for spoken Arabic language identification and discrimination between these similar languages, namely, German, Spanish, French, and Russian, all of which are taken from Mozilla speech corpus languages. Additionally, our work involves a linguistic study of these considered languages. A total of ten thousand speakers are chosen for all five languages, and the BLSTM architecture is designed and implemented using acoustic signal features and applied to five experiments in this paper. The results show a precision of 98.97%, 98.73%, 98.47%, and 99.75% for identifying the spoken Arabic language separately along with German, Spanish, French, and Russian, respectively. Additionally, we achieved an average accuracy of 95.15% for discriminating between all these considered five languages in terms of the pronunciation of words. Our findings confirm that a BLSTM architecture is able to distinguish between observable similar pronunciations of words in considered languages.

High-Order Domain Feature Extraction Technology for Ocean Acoustic Observation Signals: A Review

For the acquisition of ocean observation information, the underwater acoustic signal is the only known medium that can propagate over long distances in water. However, the underwater acoustic field environment is highly time-varying and spatially variable, and ocean acoustic observation signals are mixed Gaussian, non-cooperative, and nonlinear. Therefore, feature extraction of underwater target signals has been a challenging research direction. The high-order domain feature extraction method of ocean acoustic observation signal is widely used because of its advantages of anti-Gaussian background noise and preservation of signal phase information. Firstly, this paper analyzes three aspects, including the characteristics of ocean acoustic observation signals, the limitations of second-order domain processing methods, and advantages of high-order domain processing methods. Then, this paper focuses on theoretical advantages and application areas of signal high-order domain feature extraction technology. The technical development and application performance of common feature extraction technology in the field of ocean acoustic observation signal processing by higher-order domain methods in recent years are analyzed in the paper, and the challenges and future trends of higher-order domain feature extraction technology in marine acoustic observation signal processing are discussed in the context of recent studies.

Classification of DC discharge modes based on acoustic signal

Gas discharge will produce rich electromagnetic, optical as well as acoustic signals. Compared with the other signals, acoustic signals are also significant and would offer non-contact, low cost and easy-operation approach for online discharging monitoring, which require more attention and intensive study. In this paper, we studied the characteristics of acoustic signals in the corona, transient glow, spark, and glow discharging modes generated in a DC pin-to-pin configuration and developed a method using acoustic signals to classify the different discharge modes. The acoustic signals of the discharge at different gaps were recorded by adjusting the gap distance. 250 sets of acoustic signal samples were collected for each discharging mode. It was found that acoustic signals behave differently in different modes. Based on the short-time Fourier transform (STFT) of the acoustic signals, a novel method for discharge mode classification using the support vector machine (SVM) approach was developed. The final predictive accuracy of the trained classifier exceeds 90%.

X-ray-induced bio-acoustic emissions from cultured cells

Purpose We characterize for the first time the emission of acoustic waves from cultured cells irradiated with X-ray photon radiation. Methods and materials Human cancer cell lines (MCF-7, HL-60) and control cell-free media were exposed to 1 Gy X-ray photons while recording the sound generated before, during and after irradiation using custom large-bandwidth ultrasound transducer. The effects of dose rate and cell viability were investigated. Results We report the first recorded acoustic signals captured from a collective pressure wave response to ionizing irradiation in cell culture. The acoustic signal was co-terminous with the radiation pulse, its magnitude was dependent on radiation dose rate, and live and dead cells showed qualitatively and quantitatively different acoustic signal characteristics. The signature of the collective acoustic peaks was temporally wider and with higher acoustic power for irradiated HL-60 than for irradiated MCF-7. Conclusions We show that X-ray irradiation induces two cultured cancer cell types to emit a characteristic acoustic signal for the duration of the radiation pulse. The rapid decay of the signal excludes acoustic emissions themselves from contributing to the inter-organism bystander signal previously reported in intact animals, but they remain a potential component of the bystander process in tissues and cell cultures. This preliminary study suggests that further work on the potential role of radiation-induced acoustic emission (RIAE) in the inter-cellular bystander effect is merited.

Analog field-scale acoustic study of volcanic eruption directivity using a tiltable liquid nitrogen-charged water cannon

Laterally directed explosive eruptions are responsible for multiple fatalities over the past decade and are an increasingly important volcanology problem. To understand the energy dynamics for these events, we collected field-scale explosion data from nine acoustic sensors surrounding a tiltable cannon as part of an exploratory experimental design. For each cannon discharge, the blast direction was varied systematically at 0°, 12°, and 24° from vertical, capturing acoustic wavefield directivity related to the tilt angle. While each event was similar in energy discharge potential, the resulting acoustic signal features were variable event-to-event, producing non-repetitious waveforms and spectra. Systematic features were observed in a subset of individual events for vertical and lateral discharges. For vertical discharges, the acoustic energy had a uniform radiation pattern. The lateral discharges showed an asymmetric radiation pattern with higher frequencies in the direction of the blast and depletion of those frequencies behind the cannon. Results suggest that, in natural volcanic systems, near-field blast directionality may be elucidated from acoustic sensors in absence of visual data, with implications for volcano monitoring and hazard assessment.Graphical

Anti-interference detection and operation state identification of transformer acoustic characteristics based on Conv-Tas-ResNet

There is a limitation in the process of acoustic signal detection, which lies in serious background noise interference and the complex correlation between acoustic signal characteristics and operation states. Integrating the denoising model and feature classification model, a method of transformer acoustic signal anti-interference detection and operation state detection based on deep learning is proposed in this paper. Through tests in anechoic rooms, acoustic signals of transformers in the normal state or under harmonic load are acquired. Combining these signals with the background noise, a dataset containing 12000 samples of acoustic signals is constructed. To implement anti-interference detection, Conv-TasNet is utilized to get the transformer acoustic signal and environmental noise separated; then, ResNet is utilized to classify the operation states of the transformer accurately. Results show that compared with the blind source separation method through RNN and FastICA, the denoising model established in this paper improves Si-SDRi parameters by 37.4dB and 17.53dB respectively, and the transformer operation state classification model established in this paper classifies the test dataset with an accuracy of 97.7%, thus providing an effective method for the extraction of transformer acoustic signal and diagnosis of transformer operation states in complex environments.

Mapping Phonation Types by Clustering of Multiple Metrics

For voice analysis, much work has been undertaken with a multitude of acoustic and electroglottographic metrics. However, few of these have proven to be robustly correlated with physical and physiological phenomena. In particular, all metrics are affected by the fundamental frequency and sound level, making voice assessment sensitive to the recording protocol. It was investigated whether combinations of metrics, acquired over voice maps rather than with individual sustained vowels, can offer a more functional and comprehensive interpretation. For this descriptive, retrospective study, 13 men, 13 women, and 22 children were instructed to phonate on /a/ over their full voice range. Six acoustic and EGG signal features were obtained for every phonatory cycle. An unsupervised voice classification model created feature clusters, which were then displayed on voice maps. It was found that the feature clusters may be readily interpreted in terms of phonation types. For example, the typical intense voice has a high peak EGG derivative, a relatively high contact quotient, low EGG cycle-rate entropy, and a high cepstral peak prominence in the voice signal, all represented by one cluster centroid that is mapped to a given color. In a transition region between the non-contacting and contacting of the vocal folds, the combination of metrics shows a low contact quotient and relatively high entropy, which can be mapped to a different color. Based on this data set, male phonation types could be clustered into up to six categories and female and child types into four. Combining acoustic and EGG metrics resolved more categories than either kind on their own. The inter- and intra-participant distributional features are discussed.

Novel feature extraction method of pipeline signals based on multi-scale dispersion entropy partial mean of multi-modal component

This paper considers the problem of feature extraction of pipeline acoustic signals under different working conditions. A novel method is proposed based on multi-scale dispersion entropy partial mean (MDEPM) of multi-modal component to extract features of pipeline signals. First, variational mode decomposition (VMD) algorithm is applied to decompose the acoustic signals to obtain several mode components. Then, Kolmogorov–Smirnov distance (KSD) is introduced as the index to measure the correlation between each mode component and the original signal, and the mode component with a smaller KSD is selected as the feature component. Finally, the MDEPM of the feature component is calculated so as to form the feature vector, which realizes signal feature extraction. In experiment, 13 different condition signals collected are divided into three categories, the experimental results show that the proposed method could extract the characteristics of the different pipeline acoustic signals. Furthermore, the extracted features could be accurately identified and classified by extreme learning machine (ELM) under different working conditions, and then through comparing with other methods, the feasibility and superiority of the proposed method are verified.

A comparison of different acoustic methods for sedimentary classification of King George Bay, Antarctica

Multibeam sonar and high-resolution seismic mapping are marine geophysical techniques frequently employed to map the distribution and type of marine sediments. Both techniques involve the transmission and reception of acoustic pulses reflected by the seafloor and typically generate consistent and satisfactory results at a relatively low-cost relative to direct sampling. However, mainly due to their operational peculiarities (frequency range, beam width, etc.), each method generates some discrepancies regarding sedimentary distribution for the same area. Here, we compare three well-established marine geophysical techniques (Multibeam Echosounder (MBES) bathymetry and backscatter intensity, Sub-Bottom Profiler (SBP) echo-characterization, defined as the acoustic response of marine sediments as a function of its sedimentological properties, and seismic amplitude) applied to characterize seabed geology, analyzing comparing the results observed from each method. The study area is the glaciomarine environment of King George Bay, South Shetland Islands, Antarctica. We demonstrate that each method responds to specific characteristics of the seafloor, mainly because of divergent interactions between the acoustic signal and morphological features at different scales and sedimentological interfaces. Despite some consistencies, considerable mismatches in terms of classification approaches are apparent. Nevertheless, we show how these different classification approaches can be interpreted together to enable more precise geological understandings.

Sound propagation and scattering in turbulent media

This presentation overviews sound propagation and scattering in turbulent media and pertinent acoustic remote sensing of turbulence. Although sound propagation through a turbulent atmosphere is mainly considered, the presented results apply to other media such as oceanic turbulence. Formulations are provided for various statistical characteristics of acoustic signals such as the scattering cross section, the variances of the phase and log-amplitude fluctuations, the spatial, temporal, and cross-frequency coherences, and the probability density functions. Monte Carlo simulation of sound propagation in a random medium is explained. Specifics of acoustic signals in turbulent media as compared to electromagnetic waves are highlighted. For example, sound waves are scattered by both the sound speed and medium velocity fluctuations, which are scalar and vector random fields, respectively, with different statistical properties. Also, fluctuations in the acoustic refractive index are much stronger than those in electromagnetic propagation, and sound can be scattered by humidity (or salinity) fluctuations. Finally, the Markov approximation, which is widely used for electromagnetic waves, might not be applicable for some statistical characteristics of acoustic signals. It is explained how these specifics of sound propagation are addressed in atmospheric acoustics.

Thunder acoustic signature for channel reconstruction in triggered lightning

There are few reports on the near-field acoustic signals corresponding to lightning return strokes. One rocket-triggered lightning flash, which contains 13 return strokes, initial continuous current (ICC) processes, and abundant M-components, was observed in the summer of 2018 and reported in this study. The complete near-field acoustic pressure signals initiated by the flash were recorded and analyzed. It is found that the near-field acoustic pressure signals from each return stroke are composed of the first arrived N-shape shock waveform and the subsequent low-frequency oscillating waveforms. The characteristic of the acoustic waveforms has been parametrically defined and quantitatively analyzed, with a comprehensively acoustic-electrical correlation investigation. The acoustic pressure signals from intensive ICC processes and M-component pulses with different discharge intensities are also discussed. It is found that the too-close time interval between M-component and previous current pulses would affect the generation and measurement of the acoustic pressure waveforms from the M-component, even though the discharge amplitude is competitive and the wavefront is fast. The same phenomenon has also been observed in the acoustic signals from a return stroke with a too close interval from the previous pulse. The acoustic source localization with great accuracy for lightning channel reconstruction was realized based on the measured acoustic signals. The reconstructed channel structure agrees with that of the synchronously captured optical image. The study of the characteristics of acoustic signals from lightning discharge contributes to the recognition of thunder signals, source localization for channel reconstruction, and understanding of the lightning discharge process.

How Does a Face Mask Impact Speech Perception?

Face masks are mandatory during the COVID-19 pandemic, leading to attenuation of sound energy and loss of visual cues which are important for communication. This study explores how a face mask affects speech performance for individuals with and without hearing loss. Four video recordings (a female speaker with and without a face mask and a male speaker with and without a face mask) were used to examine individuals’ speech performance. The participants completed a listen-and-repeat task while watching four types of video recordings. Acoustic characteristics of speech signals based on mask type (no mask, surgical, and N95) were also examined. The availability of visual cues was beneficial for speech understanding—both groups showed significant improvements in speech perception when they were able to see the speaker without the mask. However, when the speakers were wearing the mask, no statistical significance was observed between no visual cues and visual cues conditions. Findings of the study demonstrate that provision of visual cues is beneficial for speech perception for individuals with normal hearing and hearing impairment. This study adds value to the importance of the use of communication strategies during the pandemic where visual information is lost due to the face mask.

Recognition of behavior state of Penaeus vannamei based on passive acoustic technology

Underwater signal processing based on passive acoustic technology has carried out a lot of research on the behavioral sound of underwater creatures and the protection of marine resources, which proves the feasibility of passive acoustic technology for long-term and non-destructive monitoring of underwater biological sound production. However, at present, most relevant research focuses on fish but little research on shrimp. At the same time, as the main economic breeding industry, Penaeus vannamei has a backward industrial structure, in which the level of intelligence needs to be improved. In this paper, the acoustic signals generated by different physiological behaviors of P. vannamei are collected based on passive acoustic technology. Their different behaviors are finally classified and identified through feature extraction and analysis. Meanwhile, the characteristic non-parametric ANOVA is carried out to explore the relationship between the acoustic signals and the behavior state of P. vannamei to achieve the purpose of real-time monitoring of the behavior state of P. vannamei. The experimental results show that linear prediction cepstrum coefficient (LPCC) and Mel-frequency cepstrum coefficient (MFCC) characteristic coefficients are effective in the classification and recognition of different behavioral acoustic signals with interspecific acoustic signals of P. vannamei. Meanwhile, the SVM classifier based on OvR classification strategy can model the acoustic signal characteristics of different underwater biological behaviors more efficiently and has classification accuracy as high as 93%.

Do the Nonlinear Dynamic Acoustic Measurements, Nonlinear Energy Difference Ratio and Spectrum Convergence Ratio, Correlate with Perceptual Evaluation of Esophageal Voice Speakers?

The acoustic assessment of phonation after total laryngectomy is challenged by signal aperiodicity which makes frequency-based acoustic measures less reliable. This is important for patients who use esophageal voice since voice samples mostly include type III (highly aperiodic) and 4 (chaotic) signals. As such, using non-linear measures, which are better suited for aperiodic phonation, may be useful to investigate the relationship between acoustic signal characteristics and perception of esophageal voice quality. This study aimed to investigate whether nonlinear dynamic acoustic methods, nonlinear energy difference Ratio (NEDR) and spectrum convergence ratio (SCR), were correlated with perceptual measures in subjects who used esophageal phonation. Thirty-one subjects who had undergone total laryngectomy and use esophageal voice as a rehabilitation method were included in this study. Expert and non-expert raters listened to the esophageal voice samples from the subjects and rated vowels and connected speech samples on a scale from 1 to 7 on dysphonia severity and intelligibility. In addition, non-linear acoustic analysis was performed to calculate NEDR and SCR. Analysis from the raters was compared to the non-linear acoustic analysis to find the correlation between the variables. There were no significant correlations between any of the non-linear acoustic measures NEDR and SCR and the perceptual ratings at the significance level of 0.05. Correlations were calculated for each acoustic measure among the expert raters and among the non-expert raters in both connected speech samples and sustained vowel fragments. In conclusion, the nonlinear dynamic acoustic analyses of spectrum convergence ratio and nonlinear energy difference ratio do not have a significant correlation with perceptual measures of esophageal voice.