Sound Signals Research Articles

Detection and diagnosis of concrete void defect using percussion-based method combined with convolutional neural network

As a fundamental construction material, concrete commonly encounter void defects, which could significantly impair the integrity of concrete members and seriously weaken their load-bearing capacity. Worse still, void defects typically exist inside concrete members, making it difficult to detect them in real time. Therefore, conducting regular inspections for internal void defects of concrete structures is of great importance to maintain structural health. This paper investigates an integrated method to detect concrete voids based on percussion approach and a convolutional neural network (CNN). Firstly, percussion-induced sound signals were collected from 18 concrete specimens with different precast void defects. Each specimen was tapped 100 times, for a total of 1,800 percussion-induced acoustic signals. Subsequently, continuous wavelet transform (CWT) and Mel frequency cepstral coefficient (MFCC) were introduced to convert the collected acoustic signals into two types of images. The converted images were divided into testing and training datasets according to the ratio of 1:3, and 600 images were input to the CNN as the test dataset and 1200 images as the training dataset. The CNN was then trained and verified by the joint input of CWT and MFCC images to perform concrete voids classification. The result shows that the proposed method presents a satisfying classification accuracy of 100 %. Next, percussion tests under 5 levels of noisy environments were conducted, and the results prove the remarkable anti-noise performance of the methodology by achieving 88.12 % accuracy even at the strongest signal-to-noise ratio of 0 dB. Finally, the superiority of the proposed method was further demonstrated through the accuracy comparison with machine learning (ML) methods, with the accuracy consistently outperforming over 25 %. In conclusion, the proposed method has a great potential for future in-situ applications in concrete void detection.

Detection technology application based on spectral subtraction and vibro acoustic principle in the measurement of ship reliability level

Introduction: As an important part of ship manufacturing, parts are of great significance in the calculation of its reliability level.Methods: To achieve rapid damage detection of ship parts, a method for measuring the reliability level of ship casting and forging parts based on spectral subtraction and vibration-acoustic principles was proposed. This method improves the spectral subtraction method by adding a percussion vibration signal and time-frequency analysis, and uses the principle of resonance acoustics to complete the construction of the test platform to obtain the natural frequency of the part and achieve non-destructive testing of the part.Results: The results show that using the Fabric data set as the task data set for experiments, the accuracy of the research method is 98.54%; the uncertainty is 5.58; the sensitivity detection is 0.26 μm. In the comparison of the spectrogram of the sound signal after noise reduction, this method has fewer yellow spots remaining on the spectrogram of the noise reduction sound signal, and almost no excess noise remains. In the comparison of modal simulation data of ship parts, the maximum relative error between the simulation data obtained by this method and the natural frequency value obtained from the experimental data is 2.3%, and there is no value exceeding 2.5%, so the error is small.Discussion: The above results show that this method can obtain more accurate natural frequencies of parts, can effectively calculate the reliability level of ship casting and forging parts, and provides a new method reference for the safe operation of ships.

Research on lung sound classification model based on dual-channel CNN-LSTM algorithm

ulmonary diseases have a significant impact on human health and life safety, and abnormalities in the lungs are a direct response to lung diseases. Establishing an effective lung sound classification model that can assist in diagnosis is of great significance for electronic auscultation.In addressing the issue of lung sound signal classification, this study introduces a deep learning classification model based on a dual-channel CNN-LSTM algorithm. Initially, Mel-scale Frequency Cepstral Coefficients (MFCC) are employed for feature extraction from the dataset, transforming lung sound signals into Mel spectrograms. On this foundation, a dual-channel algorithm classification model is constructed, with parallel Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) modules. The CNN module is designed to capture spatial dimension features of the input data, while the LSTM module focuses on temporal dimension features. These two feature sets are fused together, enabling the model to classify lung sounds and thereby assisting in diagnosing pulmonary diseases for healthcare practitioners. This experiment used the ICBHI2017 Challenge Lungs dataset and obtained 5054 pieces of data through data augmentation and sampling techniques.The results show that the accuracy, recall, and F1 score of this model reach 99.01%, 99.13%, and 0.9915, respectively, significantly superior to other models, highlighting its practical application value.

The Impact of the Fluid-Solid Coupling Behavior of Macro and Microstructures in the Spiral Cochlea on Hearing.

The cilia of the outer hair cells (OHCs) are the key microstructures involved in cochlear acoustic function, and their interactions with lymph in the cochlea involve complex, highly nonlinear, coupled motion and energy conversions, including macroscopic fluid-solid coupling. Recent optical measurements have shown that the frequency selectivity of the cochlea at high sound levels is entirely mechanical and is determined by the interactions of the hair bundles with the surrounding fluid. In this paper, an analytical mathematical model of the spiral cochlea containing macro- and micromeasurements was developed to investigate how the phonosensitive function of OHCs' motions is influenced by the macrostructural and microstructural fluid-solid coupling in the spiral cochlea. The results showed that the macrostructural and microstructural fluid-solid coupling exerted the radial forces of OHCs through the flow field, deflecting the cilia and generating frequency-selective properties of the microstructures. This finding showed that microstructural frequency selectivity arises from the radial motions of stereocilia hair bundles and enhances the hearing of sound signals at specific frequencies. It also implied that the macrostructural and microstructural fluid-solid couplings influence the OHCs' radial forces and that this is a key factor in the excitation of ion channels that enables their activity in helping the brain to detect sound.

Heart Sound Signals Classification with Image Conversion Employed

The number of patients with cardiovascular diseases worldwide is increasing rapidly, while medical resources are increasingly scarce. Heart sound classification, as the most direct means of discovering cardiovascular diseases, is attracting the attention of researchers around the world. Although great progress has been made in heart sound classification in recent years, most of them are based on traditional statistical feature methods and temporal dimension features. These traditional temporal dimension feature representation and classification methods cannot achieve good classification accuracy. This paper proposes a new partition attention module and Fusionghost module, and the entire network framework is named PANet. Without segmentation of the heart sound signal, the heart sound signal is converted into a bispectrum and input into the proposed framework for feature extraction and classification tasks. The network makes full use of multi-scale feature extraction and feature map fusion, improving the network feature extraction ability. This paper conducts a comprehensive study of the performance of different network parameters and different module numbers, and compares the performance with the most advanced algorithms currently available. Experiments have shown that for two classification problems (normal or abnormal), the classification accuracy rate on the 2016 PhysioNet/CinC Challenge database reached 97.89%, the sensitivity was 96.96%, and the specificity was 98.85%.

Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction.

Computerized breath sound based diagnostic methods are one of the emerging technologies gaining popularity in terms of detecting respiratory disorders. However, the breath sound signal used in such automated systems used to be too noisy, which affects the quality of the diagnostic interpretations. To address this problem, the proposed work presents the new hybrid approach to reject the noises from breath sound. In this method, 80 chronic obstructive pulmonary disease (COPD), 75 asthmatics and 80 normal breath sounds were recorded from the participants of a hospital. Each of these breath sound data were decontaminated using hybrid method of Butterworth band-pass filter, transient artifact reduction algorithm and spectral subtraction algorithm. The study examined the algorithms noise rejection potential over each category of breath sound by estimating the noise rejection performance metrics, i.e.,mean absolute error (MAE), mean square error (MSE), peak signal to noise ratio (PSNR), and signal to noise ratio (SNR). Using this algorithm, the study obtained a high value of SNR of 70 dB and that of PSNR of 72 dB. The study could definitely a suitable one to suppress noises and to produce noise free breath sound signal.

Monitoring Thermal Exchange of Hot Water Mass via Underwater Acoustic Tomography with Inversion and Optimization Method

Thermal exchange of underwater water mass caused by marine heat wave is a hot point of research recently. In particular, because the water temperature observation along hot water mass transportation is hard work. Acoustic tomography is an advanced method to measure water temperature variations via sound signal transmission with multi-station network sensing. The 5 kHz frequency acoustic tomography used for observing water temperature variations caused by ocean heat waves is interesting work. In this paper, the numerical simulation of hot water mass is completed first, then floatation and diffusion of hot water mass in a simulation are monitored by acoustic tomography. A new inversion optimization method is proposed to obtain hot water mass transportation variations at two-dimensional temperature vertical profile. The proposed inversion method adds a regularized mode matrix and the optimization method adds the model correlation matrix to improve the results quality. The accuracy of inversion optimization results is compared and discussed, where the mean temperature error is less than 0.4 °C. Sensing water temperature variation of marine heat waves is verified via acoustic signal transmission and improved inversion optimization method. The water dynamical process observation is an application of acoustic tomography, which can be further used observe underwater environmental characteristics.

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

Seafarers’ unsafe acts as the direct causes of maritime accidents are considered to be the result of the interaction between complex and dynamic influencing factors. Identifying the risk evolution characteristics and paths of seafarers’ unsafe acts has always been a challenge in maritime safety management. For this purpose, the present study introduces association rule technique into complex network to develop a directed weighted interaction network of seafarers’ unsafe acts and their influencing factors. Through global network topology analysis and local network community detection, the risk evolution characteristics of seafarers’ unsafe acts in maritime accidents are analyzed from a multidimensional perspective. The results indicate that the developed network has small-world characteristics, and the top 10 critical nodes all belong to seafarers’ unsafe acts, of which failure to make proper sound and light signals achieves the highest PageRank value. Results from this study would help maritime stakeholders to understand the evolution mechanism of seafarers’ unsafe acts and develop safety management strategies for interrupting the risk propagation of seafarers’ unsafe acts.

Robot assisted bone milling state classification network with attention mechanism

In the process of medical robot assisted bone milling surgery, the accuracy of recognition of milling state is crucial for surgical safety. However, previous studies have rarely used neural networks for signal analysis and processing, and not included attention mechanisms in neural networks to distinguish the weights of different signal features. In this paper a tactile-auditory attention model for milling state recognition is proposed. The model combines attention mechanism with fully connected neural networks. First, the milling state is divided into four types: idling, cortical bone, cancellous bone, and muscle. The acceleration and sound pressure information are extracted in 13 dimensions each, including 3-dimensional time-domain features and 10-dimensional frequency-domain features. Second, a milling state classification network with attention mechanism was established, including pre-connected attention mechanism (Pre-AT) and embedded attention mechanism (Emb-AT). The experimental results showed greater performance than other traditional methods, with test set accuracy of 94.57% and 95.29%, respectively. Afterwards, the impact of single signal and fused signal on recognition results was explored. From the experimental results, fused tactile-auditory signals had higher accuracy than single signal recognition. The accuracy rates of the test set using fused signals and acceleration and sound pressure signals were 95.29%, 92.09% and 90.07%. In addition, attention vectors are visualized to identify the degree of emphasis on different signals during the recognition process.

Change in sound signal propagation at the place of formation of the diffusive convection (DC) structure; in the west of the Strait of Hormuz

Double diffusion (DD) structures, in two types of diffusive convection (DC) and salt-fingering (SF), occur due to vertical temperature and salinity gradients with different diffusion coefficients. Areas such as the Strait of Hormuz, which has a thermohaline exchange between the Persian Gulf and the Sea of Oman, are suitable places for the formation of DD structures. Based on the results, the formation of DC structure is significant compared to other processes in the west of the Strait of Hormuz in December. Fluctuations in temperature and the growth of DC structure cause mixing and changing the depth of the boundary layer of temperature and salinity in the water. So that in the presence of the DC structure, the decrease in the value of the sound speed (1552 m/s) extends to a depth of 40 m, but the sound speed increases to 1555 m/s at the place where the warm water rises. The results show that the sound transmission loss increases by 5-15 dB in the place of strong DC structure. When the sound frequency increases, the transmission loss caused by the DC structure decreases. But for rays with a small propagation angle, a significant expansion is created in the propagation steps and wavelength of the rays, which increases up to 2 times with the increase in the depth of the sound source.

THE IMPORTANCE OF NON-VERBAL ELEMENTS IN ENGLISH COMMUNICATION

Non-verbal elements play a crucial role in daily life communication. Communication may be comprehended by both the sound element and signal element. In speaking a language in general and English in specific, we cannot deny the importance of non-verbal elements. This article examines the comprehension of the 3rd-year English majored undergraduates at Hung Yen University of Technology and Education of such elements. The result of the study proves that the role and a number of common non-verbal elements, under the view of cognitive aspect, has significant influence on the conveyance of messages from different communication situations in English of the English majored undergraduates in their 3rd year at university. The results of the study can be taken as a useful reference for English language learners, speakers and educators as well.

A Heart Sound Signal Classification Method Based on the Mixed Characteristics of Mel Cepstrum Coefficient and Second-Order Spectrum

A Heart Sound Signal Classification Method Based on the Mixed Characteristics of Mel Cepstrum Coefficient and Second-Order Spectrum

Realization of adaptive beamformer on open-source hardware

Realization of an efficient system for extracting useful audio signals in the presence of interferences is an important engineering problem in the field of acoustics. This paper proposes a method for extracting sound signals from a specific direction of incidence. A microphone array consisting of eight microphones was used, coupled with space-time signal processing executed in real time on hardware. The microphone signals are filtered by an adaptive beamformer, whose coefficients are optimized in real time on open-source hardware using the Least Mean Square algorithm. This paper describes the characteristics and limitations of the used microphone array. Testing the system through simulation indicates that extracting narrowband and wideband signals using the presented adaptive beamforming method is theoretically possible. This paper aims to experimentally evaluate the algorithm under real-world conditions, as well as identify the limitations of extracting wideband signals. Adaptation of the filter coefficients and filtering the signals from the microphones was implemented on Bela hardware, specialized for processing audio signals. This experiment highlights the limitations that arise from extracting a wideband signal using this type of hardware in real conditions. By comparing the useful and filtered signals in both time and frequency domains, the quality of the filtering was analyzed. Our experiments suggest that the current hardware specifications are a limiting factor for successful wideband signal filtering in real conditions, as they limit the maximum possible order of the adaptive filter, which proved to be insufficient.

Investigation on high temperature point detection of spontaneous combustion of loose coal based on optimal acoustic signal

Selecting sound source signals in acoustic temperature measurement technology is particularly important and is a key to improving the accuracy of acoustic temperature measurements. Pseudorandom, linearly swept, and pulsed signals are selected as research objects for this study. The decay of the three signals as they propagate through a loose coal, the frequency band and the period of the linearly swept signal are analyzed. The results showed that the attenuation coefficient of the linear sweep signal is less sensitive to temperature, and the attenuation coefficient of the low-frequency sound wave is smaller. When the lower limit of the frequency band of the sweep signal is closer to the upper limit of the frequency band, the main peak value of the cross-correlation coefficient is smaller. The main peak of the cross-correlation curve of low-band narrowband signals is greater than that of high-band narrowband signals. The cross-correlation effect is more significant for the 600–1000 Hz sweep signal. Moreover, the strongest effect is determined when the period is 0.1 s. Finally, the temperature of anthracite loose coal was measured according to the optimal signal. The maximum absolute error in the measurement results was 1.23 °C, and the maximum relative error was 4.22%.

End-user research into understanding perceptions of and reactions to a microarray patch (MAP) for contraception among women in Ghana, Kenya and Uganda.

Many organizations are developing new contraceptive products and approaches that promote self-care including a microarray patch (MAP) that has the potential for self-administration with appropriate training. We studied women's perceptions of the MAP technology with the primary goal of providing feedback on product attributes to inform early technical design decisions regarding various MAP contraceptive products in development by MAP developers. Our study consisted of a qualitative phase with in-person In-Depth Interviews (IDIs) with a total of 60 women of reproductive age (WRA) and quantitative surveys, via face-to-face computer-assisted interviews of a total of 927 women in Ghana, Kenya and Uganda. Women's perceptions on 12 attributes of the MAP were assessed through written descriptions, a profile, and visual stimuli such as graphics and images. Overall, the most widely preferred attribute set included: a hand-applied MAP, utilizing one circular patch, with a sticky backing, no larger than 2 cm diameter in size, applied by self, to the arm, offering sensory feedback (clicking sound and/or color change signals) to confirm enough pressure, successful application and removal, lasting 6 months with up to 12 months return to natural state of fertility. There is space to allow for variation in MAP designs (including the use of an applicator or provider administered MAP) if the design promotes and reflects the needs and expectations of users and providers. The contraceptive MAP had a high and broad level of appeal amongst all groups of women who participated in the study and has a strong value proposition around important contraceptive needs such as ease of use, convenience, and discretion.

Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis

An enhanced fault diagnosis approach for rolling bearings with composite faults using an optimized Squeeze and Excitation ResNet (SE-ResNet) model is proposed. This method integrates grid search (GS), support vector regression (SVR), ensemble empirical mode decomposition (EEMD), and low-rank multimodal fusion (LMF) to effectively handle the signals of acoustic–vibration fusion. By combining these techniques, the aim is to improve the accuracy and reliability of rolling bearing fault diagnosis. Firstly, improved EEMD combined with GS-SVR and a window function is used for rolling bearing vibration signal decomposition. Singular value methods are used to filter and reconstruct the results. Secondly, Markov transition fields (MTFs) are used to encode vibration signals into 2D images. LMF is used for the fusion of vibration and sound signals. An improved Squeeze and Excitation ResNet50 network is proposed for feature identification and classification of rolling bearing composite fault data. Finally, the method undergoes rigorous testing and evaluation using rolling bearing data. The experimental outcomes demonstrate that, in comparison to traditional neural networks, the enhanced SE-ResNet, integrated with GS-SVR-EEMD and LMF, attains superior diagnostic accuracy. Additionally, the proposed approach can be effectively utilized for diagnosing rolling bearing composite faults.

Measurement the 3D temperature distribution in the combustion zone using acoustic tomography and nolinear wavefont tracing

A non-invasive temperature measurement method in three dimensions is proposed to reconstruct the three-dimensional temperature. It relies on the use of nonlinear acoustic travel-time tomography, which incorporates acoustic path tracing, Gaussian kernel function, and modified Tikhonov regularization. Sound signals can be distorted due to large average temperature gradients and oscillatory release of heat in the combustion region. The effective acoustic signal is extracted based on spectral subtraction to evaluate the sound propagation time under distorted signals. The analysis of experimental errors indicates that the proposed acoustic pyrometry method has an average relative error of less than 15% compared to the time-averaged temperature measured by thermocouples. This result verifies the efficacy of the method when it comes to accurately reconstructing the temperature field in the combustion area. The reduction in error is attributed to the consideration of acoustic ray tracing within the proposed method.

Sound reflection patterns in the classroom of slanted floor

Speech intelligibility (SI) is the measurement of understanding the speech sound by the individuals. In the classroom, modern engineering acoustics has been discussing about the effect of SI such as the academic performance and health of students. Sound reflection in the classroom is one of the factors that affect SI and it is characterized into two types which are early reflections and late reflections. The purpose of this research was to obtain the sound reflection pattern in two classrooms with slanted floor of about the same size. It was done by capturing the sound signal at 12 measurement points in the classroom using Real Wave Analyzer (RWA) and a Smartphone. The sound signals were converted into Power Spectral Density (PSD) using Matlab to analyse the sound reflection patterns. This research also aims to observe the difference of PSD results from which the sound signals were acquired from RWA and Smartphone. It has been found that, the nearer the distance of the ground to ceiling results a higher PSD in both room sample at 1000Hz. Besides, using Smartphone results a higher PSD compared to using RWA at 500Hz. In conclusion, the nearer distance of ground to ceiling in a slanted floor classroom results a higher PSD at 1000Hz

Onboard sound emission measurements of unmanned aerial vehicles for psychoacoustic experiments

With the prospect of small UAVs being more commonly used for tasks like surveillance or parcel transport within inhabited areas, demand for noise regulations arises. The noise assessment of UAV operations can be investigated through laboratory listening experiments using auralized sound stimuli. Sound recordings of source signals for dynamic flight operations have to be performed outdoors where measurements with stationary microphones impose uncertainties in backpropagation and suffer from bad signal-to-noise ratio due to the weak source and the presence of ambient sounds. To overcome these difficulties, this contribution presents an approach for acquiring UAV source signals using onboard microphones. A lightweight setup for time synchronous recordings of sound using MEMS microphones, the UAV position and the rotational speed of each rotor was developed and applied to two quadcopters of 0.9 and 6.3 kg mass. Source directivity and propagation effects were added to the source signals to obtain clean sound pressure signals at virtual listener positions. The resulting stimuli were successfully used in a listening experiment on short-term noise annoyance.

Behavioral evidence of the modulation filterbank in an avian animal model of complex-sound perception

Behavioral detection of amplitude modulation (AM) can be adversely affected by competing AM “noise” present in the acoustic environment. Called AM masking, human studies suggest the existence of a modulation filterbank that separates sounds (signal from noise) based on differences in AM frequency. The modulation filterbank is an important theoretical advancement in hearing science because in addition to explaining AM masking, the model successfully predicts differences in speech perception across noisy listening environments with different AM statistics. Currently, there are no behavioral animal models of the modulation filterbank, presenting a serious impediment to understanding neural underpinnings. We trained budgerigars, a parakeet species, to detect AM frequencies from 64 to 400 Hz in the presence of narrowband AM maskers applied to a 2.8-kHz carrier signal. AM masker center frequencies spanned a >2 octave range centered on the target AM frequency. Behavioral AM sensitivity was evaluated with operant conditioning, a single-interval two-alternative discrimination task, and two-down one-up adaptive threshold tracking procedures. Budgerigar AM masking functions had a band-pass characteristic, consistent with the modulation filterbank and with human AM masking results for the same stimuli. To our knowledge, these are the first behaviorally estimated modulation filter shapes in a nonhuman species. [Funding: NIDCD R01-DC017519.]