Acoustic Analysis System Research Articles

Experimental Study of the Mechanical and Acoustic Emission Characteristics of Sandstone by Using High-Temperature Water-Cooling Cycles

In order to study the physical and mechanical properties of sandstone under high-temperature water-cooling cycling conditions, an RMT-150B electrohydraulic servo rock testing system and a DS-5 acoustic emission detection and analysis system were used to conduct uniaxial compression acoustic emission tests on sandstone after high-temperature water-cooling cycles. The deformation, strength, and acoustic emission characteristics of sandstone were analyzed under different temperatures and cycle times. The results show that the high-temperature water-cooling effect caused changes in the physical properties of sandstone. The volumetric expansion rate of the rock samples first decreased, then increased in temperature, and the strength first increased, then decreased, whereas the number of cycles had less of an impact on the physical properties. At 200 °C, with increased cycle number, the elastic modulus increased by 20.1%, and the compressive strength increased from 63.9 MPa to 71.46 MPa. At 300–600 °C, the elastic modulus and compressive strength of sandstone gradually decreased with increases in the temperature and cycle number, with reductions of 6.04%, 7.24%, 28.7%, 35.57%, 17.6%, 18.2%, 20.4%, and 60.5%, respectively. With increased temperature and cycle times, the acoustic emission ringing counts increased, ringing counts and cumulative energy appeared earlier, the rock samples entered elastic deformation earlier, the yield stage length increased, and the samples showed a tendency to transition from brittle to ductile damage.

Artificial Intelligence‐Powered Acoustic Analysis System for Dysarthria Severity Assessment

Dysarthria is common in movement disorders, such as Wilson's disease (WD), Parkinson's disease, or Huntington's disease. Dysarthria severity assessment is often indispensable for the management of these diseases. However, such assessment is usually labor‐intensive, time‐consuming, and expensive. To seek efficient and cost‐effective solutions for dysarthria assessment, an artificial intelligence (AI)‐powered acoustic analysis system is proposed and its performance in a valuable sample of WD, an ideal disease model with mainly mixed dysarthria, is verified. A test‐retest reliability analysis yields excellent reproducibility in the acoustic measures (mean intraclass correlation coefficient [ICC] = 0.81). Then, a system for dysarthria assessment is trained with WD patients (n = 65) and sex‐matched healthy controls (n = 65) using a machine learning approach. The system achieves reasonable performance in evaluating dysarthria severity with either stepwise classification or regression (all areas under the curve >80%; mean absolute error = 6.25, r = 0.79, p < 0.0001). The diadochokinesis and sustained phonation tasks contribute the most to prediction, and the corresponding acoustic features can provide significant and independent contributions. The present study demonstrates the feasibility and good performance of the AI‐powered acoustic analysis framework, offering the potential to facilitate early screening and subsequent management of dysarthria.

Optimised parametrisation of the MMC fracture model using AE and FE analysis

To save resources in component production, finite element (FE) simulations are state of the art during process design to prevent expensive tool modifications. For a high accuracy of a FE simulation, realistic material data is required. Unlike for flow behaviour, the determination of the fracture behaviour is not standardised. Conventionally, tensile tests with different geometries are performed, monitored with digital image correlation (DIC) and fracture is determined on the surface. However, for many specimens the initiation of fracture is assumed to occur inside the material prior to a macroscopic fracture on the surface. Therefore, various tensile tests with butterfly specimens were performed for a HCT980X steel and monitored with a DIC as well as an acoustic emission analysis (AEA) system. The displacement at fracture was evaluated conventionally for fracture on the specimen’s surface based on the DIC and unconventionally for fracture inside the specimen by AEA. FE models of the butterfly tests were created with fracture displacements of both evaluation methods as boundary conditions. Using the numerically determined stress state and fracture strain from the butterfly models, the MMC fracture model was parametrised for both evaluation methods and compared by applying both models to a FE simulation of a forming process.

MSLEFC: A low-frequency focused underwater acoustic signal classification and analysis system

Many feature extraction techniques are employed to study the classification of underwater acoustic, however, most of these techniques result in the loss of detailed information in this field. A Multi-scale short-time Fourier transform (MS-STFT) method was proposed to improve the low-frequency information and maintain the detailed information by increasing the number of channels. An effective data augmentation approach was proposed and a Ladder-like Encode (LE) architecture was built to increase the generalizability of the model and the classification accuracy for the features extracted. Finally, A Frequency-CAM (FC) method is proposed to analyze the frequency band locations that the neural network is interested in when conducting classification tasks on various classes of data. The integration of the above technologies is called MSLEFC. The performance of this system has been tested on two ship radiation noise datasets and obtained 82.94% and 96.06% accuracy, respectively. On the ShipsEar dataset, the proposed method increases the accuracy by 0.7% over the previous state-of-the-art method. The proposed model architecture has significant improvements in parameter size and computation compared to ResNet50.

Construction and recognition of acoustic ID of ancient coins based on deep learning of artificial intelligence for audio signals

In the field of cultural heritage protection, it is significant to establish a reliable ID (identifier) for valuable cultural and artistic items. At present, the identification of ancient cultural relics is mainly based on image information, such as pictures, 3D (three-dimensional) scanning, X-ray and CT (computed tomography) data. However, in many cases, it is impossible to identify whether slight damage, partial restorations, or ancient cultural relics have been replaced by fakes by using image information. In the era of digital duplication, more reliable identity information is urgently needed. The main technical challenge of an acoustic analysis system for ancient coins based on artificial intelligence technology is to find a non-destructive, fast and accurate identification method for ancient cultural relics. The recognition method includes two main modules: the artificial audio data sampling device and deep learning. In addition, this paper has completed the analysis of the vibration spectrum features of 19 ancient coins and realized the whole process of acoustic ID construction. The open-source platform Easy DL was used to analyze the multidimensional vibration spectrum curve feature extraction and identification. This method enables audio signal signature recognition technology to be applied in the display, preservation, transaction and safety management of ancient coins and other cultural relics.

A high-temperature acoustic field measurement and analysis system for determining cavitation intensity in ultrasonically solidified metallic alloys

A high-temperature acoustic field measurement and analysis system (HTAFS) was self-designed and developed to achieve real-time acoustic field analysis and quantitative cavitation characterization within high-temperature liquids. The acoustic signal was acquired by a high-temperature resistant waveguide and calibrated by separate compensation of line and continuous spectra to eliminate frequency offsets. Moreover, a new method was proposed to derive from the continuous-spectrum sound intensity and line-spectrum sound intensity in the frequency band above 1.5 times the fundamental frequency to characterize the intensity of transient cavitation and stable cavitation. The acoustic field characteristics within solidifying liquid Al-7 %Si alloy were successfully determined by this system. With the increase of ultrasound amplitude, the acoustic pressure in the alloy melt increased to be stable, the transient cavitation intensity first rose and then declined, and the stable cavitation intensity remained unchanged. Combined with the structural evolution of the primary α(Al) phase, the transient cavitation intensity was determined to be the dominant factor for the ultrasound-induced grain refinement effect.

Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects.

Application of acoustic emission of the sound frequency range is under consideration. This range is of current interest for the diagnostics of the stability of mountain slopes, glaciers, ice covers, large technical constructions (bridges, dams, etc.) as well as for the detection of rock deformation anomalies preceding earthquakes. Acoustic sensors, which can be used to record and to determine the directivity of acoustic emission of the sound frequency range, are under consideration. The structure of the system for acoustic emission recording, processing and analysis is described. This system makes it possible to determine the direction to the acoustic emission source using one multi-component sensor. We also consider the algorithms for detection of acoustic emission pulses in a noisy background, and for the analysis of their structure using the Adaptive Matching Pursuit algorithm. A method for the detection of the direction to an acoustic emission signal source based on multi-component sensors is described. The results of application of sound range acoustic emission for the detection of the intensification of rock deformations, associated with earthquake preparation and development in the seismically active region of Kamchatka peninsula, are presented.

Feasibility and basic acoustic characteristics of intelligent long-term bowel sound analysis in term neonates.

Bowel dysfunction continues to be a serious issue in neonates. Traditional auscultation of bowel sounds as a diagnostic tool in neonatal gastrointestinal problems is limited by skill and inability to document and reassess. Consequently, in order to objectively and noninvasively examine the viability of continuous assessment of bowel sounds, we utilized an acoustic recording and analysis system to capture bowel sounds and extract acoustic features in term neonates. From May 1, 2020 to September 30, 2020, 82 neonates who were hospitalized because of hyperbilirubinemia were included. For 20 h, a convolutional neural network-based acoustic recorder that offers real-time, wireless, continuous auscultation was employed to track the bowel sounds of these neonates. (1) Usable data on five acoustic parameters of bowel sound was recorded for 68 neonates, and the median values were as follows: The rate was 25.80 times/min [interquartile range (IQR): 15.63-36.20]; the duration was 8.00 s/min (IQR: 4.2-13.20); the amplitude was 0.46 (IQR: 0.27-0.68); the frequency was 944.05 Hz (IQR: 848.78-1,034.90); and the interval time was 2.12 s (IQR: 1.3-3.5). (2) In comparison to the parameters of the bowel sounds recorded from the right lower abdomen in 68 infants, the acoustic parameters of the 10 out of 68 infants from chest controls and blank controls were considerably different. (3) The 50%-75% breast milk intake group had the highest rate, the longest duration, and the highest amplitude of bowel sounds, while the >75% breast milk intake group had the highest frequency of bowel sounds. (4) Compared with neonates without hyperbilirubinemia, there was no significant difference in the five parameters of bowel sounds in hyperbilirubinemia infants; nor was there a significant effect of phototherapy and non-phototherapy status on the parameters of bowel sounds during bowel sound monitoring in hyperbilirubinemia patients. (5) A mild transient skin rash appeared on the skin of three infants. No other adverse events occurred. The acoustic recording and analysis system appears useful for monitoring bowel sounds using a continuous, invasive, and real-time approach. Neonatal bowel sounds are affected by various feeding types rather than hyperbilirubinemia and phototherapy. Potential influencing factors and the significance of their application in neonatal intestinal-related disorders require further research.

Integrated Machine Vision System for Evaluating Hole Expansion Ratio of Advanced High-Strength Steels.

In this paper, we propose a new method to estimate the hole expansion ratio (HER) using an integrated analysis system. To precisely measure the HER, three kinds of analysis methods (computer vision, punch load, and acoustic emission) were utilized to detect edge cracks during a hole expansion test. Cracks can be recognized by employing both computer vision and a punch load analysis system to determine the moment of crack initiation. However, the acoustic emission analysis system has difficulty detecting the instant of crack appearance since the magnitude of the audio signal is drowned out by noise from the press, which interrupts the differentiation of crack configuration. To enhance the accuracy for determining the HER, an integrated analysis system that combines computer vision with punch load analysis, and improves on the shortcomings of each analysis system, is newly suggested.

Analysis of the mechanical behavior of spatially reinforced composites with open holes

The paper deals with the experimental study of mechanical behavior and the common factors of deformation of structural composites in combined use of contactless optical video system and acoustic emission analysis and registration system. The experimental studies were conducted at the electromechanical testing system Instron 5989 in compliance with ASTM 5766 recommendations for the samples of spatially reinforced and fiber-laminated carbon fiber composites with open holes. The preform-based carbon plastic samples with spatially reinforced structure are obtained by 3D-weaving methods. The paper presents the results of mechanical strain tests of open-hole samples. The authors obtained stress and strain diagrams for carbon plastic of various structures and analyzed the evolution of non-homogeneous strain fields on the surface of 3D-reinforced composite samples of the studied structure. They observed the difference in mechanical behavior of spatially reinforced and fiber-laminated carbon fiber composites. The paper presents the images of the samples with the characteristic damages in the area of the opening. Using the acoustic emission signals recording data, the authors obtained the diagrams of dependence of cumulative power, which allowed to observe the diverse nature of damage accumulation for the studied groups of samples.

Cue-dependent processing in an automatic acoustic cue analysis system for detecting atypical speech

Cue-dependent processing in an automatic acoustic cue analysis system for detecting atypical speech

An Unsupervised Deep Learning System for Acoustic Scene Analysis

Acoustic scene analysis has attracted a lot of attention recently. Existing methods are mostly supervised, which requires well-predefined acoustic scene categories and accurate labels. In practice, there exists a large amount of unlabeled audio data, but labeling large-scale data is not only costly but also time-consuming. Unsupervised acoustic scene analysis on the other hand does not require manual labeling but is known to have significantly lower performance and therefore has not been well explored. In this paper, a new unsupervised method based on deep auto-encoder networks and spectral clustering is proposed. It first extracts a bottleneck feature from the original acoustic feature of audio clips by an auto-encoder network, and then employs spectral clustering to further reduce the noise and unrelated information in the bottleneck feature. Finally, it conducts hierarchical clustering on the low-dimensional output of the spectral clustering. To fully utilize the spatial information of stereo audio, we further apply the binaural representation and conduct joint clustering on that. To the best of our knowledge, this is the first time that a binaural representation is being used in unsupervised learning. Experimental results show that the proposed method outperforms the state-of-the-art competing methods.

Experimental Research on Acoustic Emission Characteristics and Felicity Effects during Coal Fatigue Failure under Cyclic Loading

In order to study the acoustic emission characteristics and Felicity effect in the process of coal fatigue failure and reveal the internal relationship between the fatigue damage evolution law and the acoustic emission activity, with the help of MTS815.02 electrohydraulic servo rock mechanics test system and PCI‐2 acoustic emission detection and analysis system, a triaxial cycling loading acoustic emission test was carried out on the coal samples. The results show that the higher the upper limit stress is, the more obvious the degree of fatigue damage will be caused by coal samples. At the same time, the more active acoustic emission signal will appear. The coal samples under linear loading are on the initial damage state, and slight fatigue, moderate fatigue, deep fatigue, and ultimate fatigue failure under cyclic loading. The acoustic emission shows the “L‐” type development evolution law in any previous stress level range, while at the last stress level, it shows the obvious “U‐” type development evolution law. The higher the frequency of the cyclic loading is, the higher the rate of initiation and expansion of the microcrack will be, while the more obvious acoustic emission phenomenon will appear. Furthermore, the ringing counting rate is basically the same as that of the energy counting rate. Under triaxial cyclic loading, a shear failure mode that extends along different directions of fracture surface will be presented. The acoustic emission in the range of different stress levels shows a different degree of Felicity effect. In contrast, it is more reasonable to use the principal stress difference as a parameter to study the Felicity effect of coal under cyclic loading.

On the Kaiser Effect of Rock under Cyclic Loading and Unloading Conditions: Insights from Acoustic Emission Monitoring

The Kaiser effect reflects the memory of the loaded rock to the irreversible damage and deformation. The stress level, loading rate and lithology are the main factors affecting the Kaiser effect of the rock. To identify the accurate stress point of the Kaiser effect, the MTS 816 rock mechanics testing system and the DS5-A acoustic emission testing and analysis system were adopted. The uniaxial cyclic loading–unloading and acoustic emission characteristic test of 90 rock specimens from three types of rocks under different stress level and loading rate was carried out. The evolution of acoustic emission under uniaxial compression of the rock corresponds to the compaction stage, elastic stage, yield stage and post-peak stress drop stage of the rock deformation and failure process and is divided into the quiet period, transition period, active period and decay period of the acoustic emission. The larger the hardness of rock is, the earlier the stress point of the Kaiser effect appears. The loading stress level (σA) has appreciable influence on the Kaiser effect of the rock. When σA ≥ 0.7σc, the Kaiser effect disappears. Usually, the dilatancy stress (crack initiation stress) does not exceed 70% of the uniaxial compressive strength (σc) of the rock, and the stress point can be the threshold to determine whether the Kaiser effect occurs. The influence of loading rate (lr) on Felicity rate (FR) is relatively large when lr < 0.01 mm/s, and FR rapidly grows with increase of the loading rate. When lr ≥ 0.01 mm/s, the influence of the loading rate on FR is relatively small. The findings facilitate the future application of the Kaiser effect and improvement of the accuracy of the acoustic emission data interpretation.

Real-Time Acoustic Voice Analysis Using a Handheld Device Running Android Operating System

We developed a highly accessible acoustic voice analysis system (VArt) using a handheld device running Android operating system. To provide stable and reliable analysis using readily obtainable equipment under unfavorable conditions, we modified the fundamental frequency (F0) extraction algorithm and designed an intuitive user interface representing a new hoarseness index (real-time Ra: Rart), which is a derivative of harmonics-to-noise ratio developed by Kojima and Shoji (Ra2). Since Rart continues to display analysis results in real time, unlike conventional acoustic analysis, it can be used for evaluation such as during phonosurgery and speech therapy. We evaluated the agreement between the earlier version of acoustic voice analysis software (VA) running on a Windows personal computer and VArt running on a handheld device. F0, Ra2, and Rart were measured in voice samples of sustained vowel phonation /a/ from 10 healthy volunteers and 22 patients with voice disorders using VA running on a Windows personal computer and VArt running on two types of handheld devices in a sound-treated room or in a medical examination room. Intraclass correlation coefficients were calculated for both systems under both conditions. All of the comparisons were highly correlated. Measurements obtained using our newly developed VArt were highly consistent with those using VA, indicating high reliability. Moreover, the new system increases the clinical feasibility of acoustic voice analysis.

Метод измерения частоты основного тона речевого сигнала для систем акустического анализа речи

Метод измерения частоты основного тона речевого сигнала для систем акустического анализа речи

Detection and estimation of population density of bean weevils (Coleoptera: Bruchidae) in stored pulses via bioacoustic analysis: Poster

Stored product insects, produce acoustic emissions by moving, feeding or ovipositing inside the grain mass. These sounds can be used not only for detection purposes, but also for population density estimation. Acoustic emissions of adults of Acanthoscelides obtectus (Coleoptera: Bruchidae) and Callosobruchus maculatus (Coleoptera: Chrysomelidae) were recorded infesting various pulses in varying population densities from 1 to 500 adults/kg product. The acoustic analysis system is being described. Population density, type of grain and pest species had significant influence on the number of sounds. The system was 100% precise in negative predictions and considerably successful in positive predictions. The system was very accurate (80-100%) in detecting insect presence even in the “critical” density of 1 adult/kg product, the most common threshold for classifying a stored mass as “infested” or “not infested”. Our study suggests that automatic monitoring of the infestation state in bulk grain is feasible in small containers. This kind of service can assist reliable decision making if it can be transferred to larger storage establishments (eg. silos). Our results are discussed on the basis of enhancing the use of acoustic sensors as a decision support system in stored product IPM.

Study on mechanical properties and acoustic emission characteristics of metallic materials under the action of combined tension and torsion

This study aims to experimentally investigate the tension–torsion failure stress,failure surface direction and acoustic emission (AE) properties for metallic materials under different loading rates. Tension and torsion combination deformation experiments on specimens of carbon steel, cast iron and the aluminum alloy were performed by using Electronic tension and torsion testing machine and acoustic emission signal detection and analysis system. Based on the theory of plane stress state, strength theory and the maximum axial force and the maximum torque collected in the test, the failure stress and failure surface direction of the material under various loading rates were calculated. Based on the least squares method, the multivariate regression model of failure stress and failure surface direction angle with respect to the tension and torsion loading rate was obtained respectively. The research results show that the established regression model for the given metal material can well predict the failure stress and failure surface direction at a given loading rate. The measured values of direction angle of failure surface and its calculated values are in good consistent with the fitted values. Acoustic emission signals of carbon steel in the stages of elastic and yield are more intensive. Both the energy signals of cast iron and the aluminum alloy reach the maximum at the fracture moment.

Optimizing Vowel Formant Measurements in Four Acoustic Analysis Systems for Diverse Speaker Groups.

This study systematically assessed the effects of select linear predictive coding (LPC) analysis parameter manipulations on vowel formant measurements for diverse speaker groups using 4 trademarked Speech Acoustic Analysis Software Packages (SAASPs): CSL, Praat, TF32, and WaveSurfer. Productions of 4 words containing the corner vowels were recorded from 4 speaker groups with typical development (male and female adults and male and female children) and 4 speaker groups with Down syndrome (male and female adults and male and female children). Formant frequencies were determined from manual measurements using a consensus analysis procedure to establish formant reference values, and from the 4 SAASPs (using both the default analysis parameters and with adjustments or manipulations to select parameters). Smaller differences between values obtained from the SAASPs and the consensus analysis implied more optimal analysis parameter settings. Manipulations of default analysis parameters in CSL, Praat, and TF32 yielded more accurate formant measurements, though the benefit was not uniform across speaker groups and formants. In WaveSurfer, manipulations did not improve formant measurements. The effects of analysis parameter manipulations on accuracy of formant-frequency measurements varied by SAASP, speaker group, and formant. The information from this study helps to guide clinical and research applications of SAASPs.

Usability of SpeechMark® landmark analysis system for teaching speech acoustics

Learning about the intersection of articulation and acoustics, and particularly acoustic measurement techniques, is challenging for students in Linguistics, Psychology and Communication Sciences and Disorders curricula. There is a steep learning curve before students can apply the material to an interesting research question; for those in more applied programs such as Communication Disorders or ESL, there is an additional challenge in envisioning how the knowledge can be applied in changing behavior. The availability of software tools such as Wavesurfer, Praat, Audacity, TF32, the University College of London software suite, among others, has made it possible for instructors to design laboratory experiences in visualization, manipulation, and measurement of speech acoustics. Many students have found them complex for their first exposure to taking scientific measurements. The SpeechMark® acoustic landmark analysis system has been developed to automate the detection of specific acoustic events important for speech, such as voicing offset and onset, stop bursts, fricative noise, and vowel midpoints, and to provide automated formant frequency measurement used for vowel space analysis. This paper describes a qualitative multiple case study in which seven teachers of speech acoustics were interviewed to explore whether such pre-analysis of the acoustic signal could be useful for teaching.