Sound Signals Research Articles

Auditory cortex conveys non-topographic sound localization signals to visual cortex

Spatiotemporally congruent sensory stimuli are fused into a unified percept. The auditory cortex (AC) sends projections to the primary visual cortex (V1), which could provide signals for binding spatially corresponding audio-visual stimuli. However, whether AC inputs in V1 encode sound location remains unknown. Using two-photon axonal calcium imaging and a speaker array, we measured the auditory spatial information transmitted from AC to layer 1 of V1. AC conveys information about the location of ipsilateral and contralateral sound sources to V1. Sound location could be accurately decoded by sampling AC axons in V1, providing a substrate for making location-specific audiovisual associations. However, AC inputs were not retinotopically arranged in V1, and audio-visual modulations of V1 neurons did not depend on the spatial congruency of the sound and light stimuli. The non-topographic sound localization signals provided by AC might allow the association of specific audiovisual spatial patterns in V1 neurons.

Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals.

Although cochlear implants can restore auditory inputs to deafferented auditory cortices, the quality of the sound signal transmitted to the brain is severely degraded, limiting functional outcomes in terms of speech perception and emotion perception. The latter deficit negatively impacts cochlear implant users' social integration and quality of life; however, emotion perception is not currently part of rehabilitation. Developing rehabilitation programs incorporating emotional cognition requires a deeper understanding of cochlear implant users' residual emotion perception abilities. To identify the neural underpinnings of these residual abilities, we investigated whether machine learning techniques could be used to identify emotion-specific patterns of neural activity in cochlear implant users. Using existing electroencephalography data from 22 cochlear implant users, we employed a random forest classifier to establish if we could model and subsequently predict from participants' brain responses the auditory emotions (vocal and musical) presented to them. Our findings suggest thatconsistent emotion-specific biomarkers exist in cochlear implant users, which could be used to develop effective rehabilitation programs incorporating emotion perception training. This study highlights the potential of machine learning techniques to improve outcomes for cochlear implant users, particularly in terms of emotion perception.

Enhancing Insect Sound Classification Using Dual-Tower Network: A Fusion of Temporal and Spectral Feature Perception

In the modern field of biological pest control, especially in the realm of insect population monitoring, deep learning methods have made further advancements. However, due to the small size and elusive nature of insects, visual detection is often impractical. In this context, the recognition of insect sound features becomes crucial. In our study, we introduce a classification module called the “dual-frequency and spectral fusion module (DFSM)”, which enhances the performance of transfer learning models in audio classification tasks. Our approach combines the efficiency of EfficientNet with the hierarchical design of the Dual Towers, drawing inspiration from the way the insect neural system processes sound signals. This enables our model to effectively capture spectral features in insect sounds and form multiscale perceptions through inter-tower skip connections. Through detailed qualitative and quantitative evaluations, as well as comparisons with leading traditional insect sound recognition methods, we demonstrate the advantages of our approach in the field of insect sound classification. Our method achieves an accuracy of 80.26% on InsectSet32, surpassing existing state-of-the-art models by 3 percentage points. Additionally, we conducted generalization experiments using three classic audio datasets. The results indicate that DFSM exhibits strong robustness and wide applicability, with minimal performance variations even when handling different input features.

Blast Exposure Alters Synaptic Connectivity in the Mouse Auditory Cortex.

Blast exposure can cause auditory deficits that have a lasting, significant impact on patients. Although the effects of blast on auditory functions localized to the ear have been well documented, the impact of blast on central auditory processing is largely undefined. Understanding the structural and functional alterations in the central nervous system (CNS) associated with blast injuries is crucial for unraveling blast-induced pathophysiological pathways and advancing development of therapeutic interventions. In this study, we used electrophysiology in combination with optogenetics assay, proteomic analysis, and morphological evaluation to investigate the impairment of synaptic connectivity in the auditory cortex (AC) of mice following blast exposure. Our results show that the long-range functional connectivity between the medial geniculate nucleus (MGN) and AC was impaired in the acute phase of blast injury. We also identified impaired synaptic transmission and dendritic spine alterations within 7 days of blast exposure, which recovered at 28 days post-blast. Additionally, proteomic analysis identified a few differentially expressed proteins in the cortex that are involved in synaptic signaling and plasticity. These findings collectively suggest that blast-induced alterations in the sound signaling network in the auditory cortex may underlie hearing deficits in the acute and sub-acute phases after exposure to shockwaves. This study may shed light on the perturbations underlying blast-induced auditory dysfunction and provide insights into the potential therapeutic windows for improving auditory outcomes in blast-exposed individuals.

Influence of the relative position of the Parus-A ionosonde antennas on the results of oblique sounding

The article considers the issue of relevance of ground-based equipment for ionosphere sounding and investigates some parameters of the ionospheric network of Roshydromet, equipped with Russian ionosondes “Parus-A”. The analysis of antenna systems used in these ionosondes has been carried out. The peculiarities of the reception of oblique sounding signals depending on the mutual arrangement of the antennas have been analyzed. A proposal has been made to take these issues into account in the development of new domestic ionosondes.

Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter

The most significant issues for a thousand users is the unwanted or loud signals in sound files, it is impossible to eliminate or minimize these noise signals without knowledge their types and ranges. To overcome this issue, present an Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter (EIS-SNR-VBRAF) is proposed. Initially, the data is collected from Adaptive Myriad Filter using Time-Varying Noise and Signal-Dependent Parameters dataset. Then, the data is given for Pre-processing section, Variational Bayesian based Robust Adaptive Filter (VBRAF) is used to reduce the signal noise from Gaussian Noise, Traffic noise and Audio signal back ground noise. The proposed technique is implemented and efficacy of EIS-SNR-VBRAF technique is assessed by support of numerous performances such as BER, Signal-to-Noise-Ratio (SNR), Mean Squared Error, Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error, Structural Similarity Index, Cross-Correlation and Computational Complexity is analyzed. The proposed EIS-SNR-VBRAF method attains 21.18%, 23.52% and 23.65% lower RMSE, 21.52%, 21.76% and 23.24% higher PSNR, 21.19%, 21.73% and 20.48% higher Structural Similarity Index are compared with existing methods like early detection of mechanical malfunctions in vehicles utilizing sound signal processing (ED-MMV-SSP),noise reduction in infrasound signals based on mask coefficient binary weighting generalized cross correlation non-negative matrix factorization algorithm (NRID-MCWG-NMFA) and Click-event sound detection in automotive industry using machine/deep learning (CE-SDSI-DL) respectively.

Time difference auto‐extraction methods for in situ sound speed measurements in seafloor sediments

AbstractThe in situ acoustic measurement of seafloor sediment is an important technical means to obtain the acoustic parameters of seafloor. The time‐of‐flight method is commonly used to calculate the sound speed in seafloor sediment. Accurate identification of signal feature points is essential for determining travel time or travel time difference of acoustic signals. However, the precise identification of feature points, such as the take‐off point of the first wave of a sound wave signal, is challenging. The conventional manual identification method is inefficient and prone to errors. The development of a feature point auto‐identification method is imperative for accurately calculating the travel time of acoustic signals. In this study, we employed the cross‐correlation method, the level threshold method and the short window‐long window energy ratio method to extract the acoustic travel time differences and calculate the sound speeds in seawater and in seafloor sediment. We then analysed the effectiveness of these calculated results. The sound speeds in seawater obtained through the aforementioned methods were compared with the sound speeds measured using a sound velocity profiler. The comparison revealed that these processing methods exhibit a high level of accuracy. The sound speed results in sediments show that the programme‐based auto‐identification methods significantly reduce the standard deviation compared to the manual identification method. This study successfully assessed the processing accuracy of different methods and expanded the processing methods for in situ acoustic signals of seafloor sediments.

Voice Recognition to Classify “Buka” and “Tutup” Sound to Open and Closes Door Using Mel Frequency Cepstral Coefficients (MFCC) and Convolutional Neural Network (CNN)

The consequences of the coronavirus called COVID-19 have been really impactful on society. Many things need to be changed in order to survive this pandemic. People have to avoid physical contact to minimize the probability of getting caught by other people who have been infected. A doorknob has a really big potential to be the medium to spread the virus because the same surface is used by several people. Speech recognition can be used to solve this problem. In this study, Mel Frequency Cepstral Coefficients (MFCC) and Convolutional Neural Network (CNN) are going to be used as the extraction feature and classification method, respectively. We classify the sound signal into two classes (“buka” and “tutup”). People who want to open or close the door just need to say a specific command. This can be helpful to minimize the risk of COVID transmission. A CNN model is developed and fed with an audio file from a curated dataset for training and testing. With this system, we have successfully trained the model with an accuracy of 89% using an epoch of 50 and batch size of 32 as the parameters with a dataset distribution of 8:2 for training and validation. We believe this study will be influential in developing automated door systems using speech recognition, especially in the Indonesian language.

Computational hemodynamics and hemoacoustic study on carotid bifurcation: Effect of stenosis and branch angle

Investigation of sound-signal-based noninvasive diagnosis of arterial stenosis is an active area of research. This study focuses on computational investigation of hemodynamic and hemoacoustic parameters within the carotid bifurcation. The objective is to analyze the effect of 40 distinct geometric configurations on indicative sound signals, useful for understanding the feasibility of stethoscope-based diagnosis of stenosis. The study employs an in-house flow-solver based on the semi-implicit pressure-projection method on a curvilinear grid. Physiological condition-based pulsatile flow waveforms and three-element Windkessel model-based pressure are utilized at the inlet and outlets of the bifurcating carotid artery. The research involves assessment of parameters like wall shear stress (WSS) and integrated pressure force rate (IPFR) fast Fourier transform (FFT) spectrum. Geometric configurations are varied based on stenosis level S (0, 45%, 60%, and 70%), bifurcation angle BA (30°, 40°, 50°, and 65°), and length of stenosis L (1, 1.5, and 2). In the investigated geometries, WSS exhibits a distinct behavior, reaching a peak at stenosis and subsequently transitioning to a negative value. Furthermore, IPFR-spectrum analysis reveals distinguishable frequencies for S≥ 40%, hinting at the potential for stethoscope-based diagnosis. A novel correlation between the cutoff frequencies of IPFR FFT-spectrum and arterial geometry is established, which reflect the influence of artery geometry on sound signals. Computational fluid dynamics (CFD)-based flow-visualization approach is proposed to calculate characteristic frequencies, which are close to IPFR spectrum frequencies. Our study contributes to a framework for potential sound-based classification of plaque-induced constrictions.

Cloning, characterization, and evolutionary patterns of KCNQ4 genes in anurans.

Acoustic communication plays important roles in the survival and reproduction of anurans. The perception and discrimination of conspecific sound signals of anurans were always affected by masking background noise. Previous studies suggested that some frogs evolved the high-frequency hearing to minimize the low-frequency noise. However, the molecular mechanisms underlying the high-frequency hearing in anurans have not been well explored. Here, we cloned and obtained the coding regions of a high-frequency hearing-related gene (KCNQ4) from 11 representative anuran species and compared them with orthologous sequences from other four anurans. The sequence characteristics and evolutionary analyses suggested the highly conservation of the KCNQ4 gene in anurans, which supported their functional importance. Branch-specific analysis showed that KCNQ4 genes were under different evolutionary forces in anurans and most anuran lineages showed a generally strong purifying selection. Intriguingly, one significantly positively selected site was identified in the anuran KCNQ4 gene based on FEL model. Positive selection was also found along the common ancestor of Ranidae and Rhacophoridae as well as the ancestral O. tianmuii based on the branch-site analysis, and the positively selected sites identified were involved in or near the N-terminal ion transport and the potassium ion channel functional domain of the KCNQ4 genes. The present study revealed valuable information regarding the KCNQ4 genes in anurans and provided some new insights for the underpinnings of the high-frequency hearing in frogs.

Experimental Validation of Reconstruction of Sound Source Radiation in an Underwater Half-Space Bounded by a Water-Air Interface

When a vibrating structure is located in a half-space bounded by a reflecting surface, the measured sound pressures are polluted by the reflection, so that they cannot show the actual sound radiation from the structure. The half-space spherical wave function expansion-based near-field acoustical holography approach is proposed to reconstruct the direct radiation from the sound source using the polluted sound pressures, which has been validated by numerical simulations. This paper deals with the experimental validation of this approach applied in an underwater half-space bounded by a water-air interface. The sound pressures measured in the half-space are used as the input data. The reconstructed sound pressures obtained are compared with benchmarks measured in a free space. The results indicate that the influences of the water-air interface reflection on the measured sound pressures can be greatly reduced in the entire frequency range investigated. The normalized relative reconstruction error introduced by the parameters of the hydrophone array and the water tank in the experiment is analyzed. The results of the experimental validation show that the proposed approach is promising for accurately measuring sound signals radiated from manned or unmanned vehicles or equipment underwater near a water-air interface.

SUBSYSTEM FOR MONITORING ATMOSPHERIC AIR QUALITY IN THE CYBER-PHYSICAL SYSTEM "SMART CITY"

The task of designing and developing a cyber-physical system "Smart City" is currently relevant for Ukraine. This study is devoted to the development of a method and subsystem for monitoring atmospheric air quality in the cyber-physical system "Smart City". The article develops a method for monitoring atmospheric air quality, which forms the basis for effective monitoring of atmospheric air quality in the cyber-physical system "Smart City" and allows making informed decisions on warning residents about the danger with recommendations for protecting their health. The developed subsystem for monitoring atmospheric air quality in the cyber-physical system “Smart City” collects data from the installed sensors of air humidity, air temperature, dust content in the air, air radiation background, air pollution level by nitrogen oxides, air pollution level by sulfur, air pollution level by carbon compounds, air pollution level by greenhouse gases CO, CO2, NH3, NO, PM2. 5, PM10, real-time transmission of the collected data to the data processing server, real-time processing and analysis of the received data using various analytical methods, visualization of the air quality monitoring results in the form of a city map with n districts displaying all air parameters. The user can select the air parameters of interest in the mobile application of the cyber-physical system. After selecting such parameters, the visualization of the air quality monitoring results is adapted to the user's needs: the measured value of the parameter selected by the user is displayed on the image of the district on the city map, and the mobile application displays a sound signal in the background and a flashing sign on the image of the district on the city map in the application, which signals a danger in this area of the city; clicking on this sign displays a notification on the screen about the indicator for which there is a danger and recommendations for protecting the health of residents in this case.

Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning.

The elevator door system plays a crucial role in ensuring elevator safety. Fault prediction is an invaluable tool for accident prevention. By analyzing the sound signals generated during operation, such as component wear and tear, the fault of the system can be accurately determined. This study proposes a GNN-LSTM-BDANN deep learning model to account for variations in elevator operating environments and sound signal acquisition methods. The proposed model utilizes the historical sound data from other elevators to predict the remaining useful life (RUL) of the target elevator door system. Firstly, the opening and closing sounds of other elevators is collected, followed by the extraction of relevant sound signal characteristics including A-weighted sound pressure level, loudness, sharpness, and roughness. These features are then transformed into graph data with geometric structure representation. Subsequently, the Graph Neural Networks (GNN) and long short-term memory networks (LSTM) are employed to extract deeper features from the data. Finally, transfer learning based on the improved Bhattacharyya Distance domain adversarial neural network (BDANN) is utilized to transfer knowledge learned from historical sound data of other elevators to predict RUL for the target elevator door system effectively. Experimental results demonstrate that the proposed method can successfully predict potential failure timeframes for different elevator door systems.

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.