Sound Analysis Research Articles

The Potential of Sound Analysis to Reveal Hemodynamic Conditions of Arteriovenous Fistulae for Hemodialysis.

Arteriovenous fistula (AVF), the preferred vascular access for hemodialysis, is associated with high failure rate. The aim of this study was to investigate the potential of AVF sound auscultation in providing quantitative information on AVF hemodynamic conditions. This single-center prospective study involved six patients with native radio-cephalic AVFs who underwent multiple follow-up visits. Doppler Ultrasound blood flow volume (BFV) assessment and electronic stethoscope-based sound recordings were performed during each visit, whereas MRIs were acquired 3days, 3weeks and 1year after surgery. Computational fluid dynamic (CFD) simulations were performed on patient-specific MRI-derived geometrical models. Higher values of median peak amplitudes ratios (high-low peak ratio-HLPR) were found to be associated with complex blood flow and velocity streamlines recirculation at systolic peak, and corresponding extended regions of high oscillatory shear index (OSI). On the contrary, lower values of HLPR were associated with laminar flow pattern and low values of OSI. Significant differences were observed in HLPR between subgroups with extended or limited areas with OSI > 0.1 (0.67 vs 0.31, respectively). Significant relationships were found between AVF sound intensity and brachial BFV (slope = 0.103, p < 0.01) as well as between longitudinal changes in brachial BFV and HLPR (slope = - 0.001, p < 0.01). Our results show that AVF sound can be exploited to extract fundamental information on AVF hemodynamic conditions, providing indication of the presence of complex hemodynamic and adequate BFV to perform hemodialysis. Sound analysis has therefore the potential to improve clinical AVF surveillanceand to ameliorate outcome.

Feasibility of Acoustic Features of Vowel Sounds in Estimating the Upper Airway Cross Sectional Area during Wakefulness: A Pilot Study

Assessment of upper airway dimensions has shown great promise in understanding the pathogenesis of obstructive sleep apnea (OSA). However, the current screening system for OSA does not have an objective assessment of the upper airway. The assessment of the upper airway can accurately be performed by MRI or CT scans, which are costly and not easily accessible. Acoustic pharyngometry or Ultrasonography could be less expensive technologies, but these require trained personnel which makes these technologies not easily accessible, especially when assessing the upper airway in a clinic environment or before surgery. In this study, we aimed to investigate the utility of vowel articulation in assessing the upper airway dimension during normal breathing. To accomplish that, we measured the upper airway cross-sectional area (UA-XSA) by acoustic pharyngometry and then asked the participants to produce 5 vowels for 3 seconds and recorded them with a microphone. We extracted 710 acoustic features from all vowels and compared these features with UA-XSA and developed regression models to estimate the UA-XSA. Our results showed that Mel frequency cepstral coefficients (MFCC) were the most dominant features of vowels, as 7 out of 9 features were from MFCC in the main feature set. The multiple regression analysis showed that the combination of the acoustic features with the anthropometric features achieved an R2 of 0.80 in estimating UA-XSA. The important advantage of acoustic analysis of vowel sounds is that it is simple and can be easily implemented in wearable devices or mobile applications. Such acoustic-based technologies can be accessible in different clinical settings such as the intensive care unit and can be used in remote areas. Thus, these results could be used to develop user-friendly applications to use the acoustic features and demographical information to estimate the UA-XSA.

Experimental investigation on the post-fire mechanical properties of YSt-355FR (0.1 % Mo) low-alloyed fire-resistant steel

Fire-resistant steel (YSt-355FR (0.1 % Mo)) has become the most utilized structural steel due to its superior performance at elevated temperatures compared to traditional structural steel at both the material and structural levels. However, its performance after exposure to fire remains questionable. This study investigates the post-fire performance of cold-formed fire-resistant steel at the material level. A total of 132 steel coupons and 84 Charpy test specimens were exposed to temperatures ranging from 100°C to 1000°C. These temperature-exposed steel coupons were subjected to four different cooling conditions: furnace cooling (FC), air cooling (AC), water-jet cooling (WJC), and water cooling (WC). Tensile tests were performed on the cooled steel coupons to obtain post-fire stress-strain curves, from which the residual mechanical properties were determined. Charpy impact tests were also conducted on the cooled steel specimens to obtain impact energies and identify the fracture patterns. Key observations regarding the post-fire behavior of fire-resistant steel in terms of strength, deformability, stiffness, and toughness parameters were made. Both the exposure temperature and the cooling condition significantly influence the post-fire performance of fire-resistant steel. Fire-resistant steel demonstrates superior post-fire behavior compared to traditional structural steel. Constitutive equations were proposed to predict the mechanical properties of fire-resistant steel under various post-fire conditions, and a sound reliability analysis was performed to examine the reliability of the predicted design parameters compared to the test results. The findings presented in this paper are significant as no prior investigation has assessed the post-fire performance of fire-resistant steel; thus, they may encourage the steel construction industry to prefer fire-resistant steel over traditional structural steels and promote the reuse of temperature-exposed fire-resistant steel for sustainable construction.

THE EVENT: ON SCREEN, REPRODUCTIVE RIGHTS

This article analyzes the feature film The Happening (Audrey Diwan, 2021) based on the journey and experiences of its protagonist, a young French woman in the 1960s who seeks a safe abortion despite living in a patriarchal context marked by oppression, condemnation of women's bodies, and violence. The aim is to relate the aesthetic, visual, and sound analysis to the current and urgent debate on women's reproductive rights, with an emphasis on abortion. The theoretical framework is anchored in contemporary feminist theory and criticism, especially Federici (2018) and Diniz (2020), and the methodology of film analysis is understood here as a recreation of the original narrative, an exercise that prioritizes passages and frames allied to interlocutions with the theme addressed.

Study of vowel formant data using Gabmap: A pilot analysis of a Lithuanian dialect

Abstract The article reviews the possibilities of applying the web application Gabmap developed by researchers at the University of Groningen to the analysis of vowel formant data based on the results of the study of one Lithuanian dialect. The Western Aukštaitian dialect of Kaunas is traditionally considered the basis of Standard Lithuanian. The study was based on the data of 18 male speakers from six dialectal localities representing distinctive areas. The normalized mean values of the first two vowel formants (F1, F2) were analyzed in certain word positions illustrating distinguishing features, which were processed using Gabmap tools designed for analyzing numeric sequences. Gabmap tools most beneficial for the researchers of experimental phonetics along with the aspects of their application to research were emphasized in the study; it also underlined those tools the use of which requires additional application of other conventional methods of experimental sound analysis, such as sound spectrum analysis, planimetric models, etc. In the study of the Western Aukštaitian of Kaunas, the Gabmap analysis confirmed significant horizontal variation within the dialect area and foregrounded the most tightly related sites sharing the greatest number of common features in their vocalism.

Empowering Healthcare: TinyML for Precise Lung Disease Classification

Respiratory diseases such as asthma pose significant global health challenges, necessitating efficient and accessible diagnostic methods. The traditional stethoscope is widely used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung auscultation. However, it has limitations, such as a lack of recording functionality, dependence on the expertise and judgment of physicians, and the absence of noise-filtering capabilities. To overcome these limitations, digital stethoscopes have been developed to digitize and record lung sounds. Recently, there has been growing interest in the automated analysis of lung sounds using Deep Learning (DL). Nevertheless, the execution of large DL models in the cloud often leads to latency, dependency on internet connectivity, and potential privacy issues due to the transmission of sensitive health data. To address these challenges, we developed Tiny Machine Learning (TinyML) models for the real-time detection of respiratory conditions by using lung sound recordings, deployable on low-power, cost-effective devices like digital stethoscopes. We trained three machine learning models—a custom CNN, an Edge Impulse CNN, and a custom LSTM—on a publicly available lung sound dataset. Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. This advancement facilitates the prospect of automated respiratory health screening using lung sounds.

High performance method for COPD features extraction using complex network.

Objectives. The paper proposes a novel methodology for the classification of Chronic Obstructive Pulmonary Disease (COPD) utilizing respiratory sound attributes.Methods. The approach involves segmenting respiratory sounds into individual breaths and conducting extensive studies on this dataset. Spectral Transforms, various Wavelet Transforms are applied to capture distinct signal features. Complex Network is also employed to extract characteristic elements, generating novel representations of spectrogram data based on graph factors, including entropy, density, and position. The normalized and enriched data is then used to develop COPD classifiers using six machine learning algorithms, fine-tuning with appropriate training details and hyperparameter tuning.Results. Our results demonstrate robust performance, with ROC curves consistently exhibiting an Area Under the Curve (AUC) > 96% across different time-frequency transformations. Notably, the Random Forest algorithm achieves an AUC of 99.67%, outperforming other algorithms. Moreover, the Wavelet Daubechies 2 (Db2) consistently approaches 98% accuracy, particularly noteworthy in conjunction with the Naive Bayes algorithm.Conclusion. This study diagnosis patients through spectrogram images extracted from lung sounds. The application of Inverse Transforms, Complex Network, and Optimized Classification Algorithms yielded results beyond expectations. This methodology provides a promising approach for accurate COPD diagnosis, leveraging Machine Learning techniques applied to respiratory sound analysis.

A dataset of Solicited Cough Sound for Tuberculosis Triage Testing

Cough is a common and commonly ignored symptom of lung disease. Cough is often perceived as difficult to quantify, frequently self-limiting, and non-specific. However, cough has a central role in the clinical detection of many lung diseases including tuberculosis (TB), which remains the leading infectious disease killer worldwide. TB screening currently relies on self-reported cough which fails to meet the World Health Organization (WHO) accuracy targets for a TB triage test. Artificial intelligence (AI) models based on cough sound have been developed for several respiratory conditions, with limited work being done in TB. To support the development of an accurate, point-of-care cough-based triage tool for TB, we have compiled a large multi-country database of cough sounds from individuals being evaluated for TB. The dataset includes more than 700,000 cough sounds from 2,143 individuals with detailed demographic, clinical and microbiologic diagnostic information. We aim to empower researchers in the development of cough sound analysis models to improve TB diagnosis, where innovative approaches are critically needed to end this long-standing pandemic.

Framework of acoustic analysis and shape optimization for three-dimensional doubly periodic multilayered structures

In this research, a framework of acoustic analysis and shape optimization, based on isogeometric boundary element method (IGA-BEM), is proposed for three-dimensional doubly periodic multilayered structures. The study addresses a gap in the literature by focusing on the shape optimization of such structures, which has not been extensively explored previously. The interface between different acoustic media is an infinite doubly periodic surface, which can be constructed by an open non-uniform rational B-splines. A periodic IGA-BEM is developed for the sound field analysis of the doubly periodic multilayered structure, in which the Ewald method is used to accelerate the calculation of periodic Green function. Furthermore, the shape derivative of the doubly periodic multiple boundaries is derived by imposing boundary perturbation and using the adjoint variable method. The control points of the NURBS surfaces are defined as the shape design variables, and all shape sensitivities can be quickly calculated by discretizing the shape derivative formula. Finally, in according with shape sensitivities, the corresponding shape optimization problem is solved by the method of moving asymptotes, so that the optimized shape design can be obtained. A series of numerical examples validates the accuracy and applicability of the proposed approaches.

Sound and Partially-Complete Static Analysis of Data-Races in GPU Programs

GPUs are progressively being integrated into modern society, playing a pivotal role in Artificial Intelligence and High-Performance Computing. Programmers need a deep understanding of the GPU programming model to avoid subtle data-races in their codes. Static verification that is sound and incomplete can guarantee data-race freedom, but the alarms it raises may be spurious and need to be validated. In this paper, we establish a True Positive Theorem for a static data-race detector for GPU programs, i.e., a result that identifies a class of programs for which our technique only raises true alarms. Our work builds on the formalism of memory access protocols, that models the concurrency operations of CUDA programs. The crux of our approach is an approximation analysis that can correctly identify true alarms, and pinpoint the conditions that make an alarm imprecise. Our approximation analysis detects when the reported locations are reachable (control independence, or CI), and when the reported locations are precise (data independence, or DI), as well identify inexact values in an alarm. In addition to a True Positive result for programs that are CI and DI, we establish the root causes of spurious alarms depending on whether CI or DI are present. We apply our theory to introduce FaialAA, the first sound and partially complete data-race detector. We evaluate FaialAA in three experiments. First, in a comparative study with the state-of-the-art tools, we show that FaialAA confirms more DRF programs than others while emitting 1.9× fewer potential alarms. Importantly, the approximation analysis of FaialAA detects 10 undocumented data-races. Second, in an experiment studying 6 commits of data-race fixes in open source projects OpenMM and Nvidia’s MegaTron, FaialAA confirmed the buggy and fixed versions of 5 commits, while others were only able to confirm 2. Third, we show that 59.5% of 2,770 programs are CI and DI, quantifying when the approximation analysis of FaialAA is complete. This paper is accompanied by the mechanized proofs of the theoretical results presented therein and a tool (FaialAA) implementing of our theory.

The ART of Sharing Points-to Analysis: Reusing Points-to Analysis Results Safely and Efficiently

Data-flow analyses like points-to analysis can vastly improve the precision of other analyses, and enable powerful code optimizations. However, whole-program points-to analysis of large Java programs tends to be expensive – both in terms of time and memory. Consequently, many compilers (both static and JIT) and program-analysis tools tend to employ faster – but more conservative – points-to analyses to improve usability. As an alternative to such trading of precision for performance, various techniques have been proposed to perform precise yet expensive fixed-point points-to analyses ahead of time in a static analyzer, store the results, and then transmit them to independent compilation/program-analysis stages that may need them. However, an underlying concern of safety affects all such techniques – can a compiler (or program analysis tool) trust the points-to analysis results generated by another compiler/tool? In this work, we address this issue of trust in the context of Java, while accounting for the issue of performance. We propose ART: Analysis-results Representation Template – a novel scheme to efficiently and concisely encode results of flow-sensitive, context-insensitive points-to analysis computed by a static analyzer for use in any independent system that may benefit from such a precise points-to analysis. ART also allows for fast regeneration of the encoded sound analysis results in such systems. Our scheme has two components: (i) a producer that can statically perform expensive points-to analysis and encode the same concisely, (ii) a consumer that, on receiving such encoded results (called artwork), can regenerate the points-to analysis results encoded by the artwork if it is deemed “safe”. The regeneration scheme completely avoids fixed-point computations and thus can help consumers like static analyzers and JIT compilers to obtain precise points-to information without paying a prohibitively high cost. We demonstrate the usage of ART by implementing a producer (in Soot) and two consumers (in Soot and the Eclipse OpenJ9 JIT compiler). We have evaluated our implementation over various benchmarks from the DaCapo and SPECjvm2008 suites. Our results demonstrate that using ART, a consumer can obtain precise flow-sensitive, context-insensitive points-to analysis results in less than (average) 1% of the time taken by a static analyzer to perform the same analysis, with the storage overhead of ART representing a small fraction of the program size (average around 4%).

Comparison of measurement of integrated relaxation pressure by esophageal manometry with analysis of swallowing sounds with artificial intelligence in patients with achalasia.

Esophageal motility disorders are mainly evaluated with high-resolution manometry (HRM) which is a time-consuming and uncomfortable procedure with potential adverse events. Acoustic characterization of the swallowing has the potential to be an alternative noninvasive procedure. We compared the findings on HRM and swallowing sounds in 43 patients who were referred for evaluation of dysphagia. The sound analysis was done with empirical mode decomposition method and with artificial intelligence (AI) and the estimated integrated relaxation pressure (IRP) from a two-layer neural network method was compared to measured IRP on HRM. The model then was tested in five patients. IRP was estimated with high accuracy using the model developed with two-layer neural network method. The analysis of acoustic properties of swallowing has the potential to be used for evaluation of esophageal motility disorders, this needs to be further evaluated in larger studies.

Contribution of machine learning and physics-based sound simulations for the optimization of brass instruments

Sound simulations by physical model are interesting to transcribe the physics underlying the functioning of a musical instrument. These simulations make it possible to create virtual sounds with a mode of operation representative of the interaction musician/instrument (driving a sound by the causes that create it). The work consists in studying the contribution of machine learning (ML) methods in the optimization of a musical instrument. The application concerns the brass instruments (trumpets). From a training set of virtual instruments, ML methods are trained to model the intonation (represented by the equivalent fundamental pitch) and the ease of emission (represented by the threshold pressure obtained by linear stability analysis) of sounds, according to the modal parameters of the input impedance. With the ML models, an optimization with genetic algorithms is next carried out to improve the intonation and the ease of emission. The last stage of the approach consists in the optimization of the bore of the instrument (optimization of the shape of the leadpipe). The results show that the approach gives promising results: optimal instruments are actually efficient when re-simulated with the physical model. The manufacturing of an optimal instrument is in progress to confirm the validity of the approach

Sound insulation analysis of sound insulation materials laminated with membrane vibration type acoustic metamaterial

In recent years, research on various acoustic metamaterials has progressed. We fabricated PP into a honeycomb structure and pasted films on the top and bottom to create an acoustic metamaterial that has a sound-absorbing effect due to membrane vibration. Furthermore, a small hole was made in the center of the film part to add sound absorption effect through Helmholtz resonance. Considering its application to automobile trim, felt and rubber layers were added and laminated on a steel plate. This structure was modeled using the finite element method and the transmission loss was calculated. We will report on comparisons with measurement results and calculation results for various lamination patterns.

A study on the difference in noise reduction between rear and side microphone on CPX measurement method through sound absorption frequency analysis

The road noise evaluation method in Korea is based on the 'noise vibration process test standard' by the Ministry of Environment. In this evaluation method, the noise felt at the sound-receiving point is measured as LAeq. Recently, in order to clarify the noise measurement standards for low-noise pavements, an international standard (ISO11819-2, CPX method) was first introduced in the Porous Pavement Guidelines of the Ministry of Land, Infrastructure and Transport. According to ISO, CPX measurement method adopts side microphones as mandatory, and the rear microphone as optional. Due to the difference in location noise level is different, causing confusion in the road noise evaluation method. Researchers have published several studies in the past that the rear microphone is highly correlated with the equivalent noise level method based on the Korean standard. As a follow-up study to the above results, this study examines the characteristics and causes of the frequency spectrum of the rear and side microphones in the case of dense asphalt pavement and porous asphalt pavement and derives the results through frequency analysis of the sound absorption coefficient test to determine and to contribute improving reliability and institutional rationalization of the CPX measurement method.

Comparison of the noise perception of conventional aircraft and Unmanned Aircraft Systems

The use of Unmanned Aircraft Systems (UAS) for commercial applications is seeing steady growth in numerous sectors as the technology improves and more applications are realised. Whilst research into human response to UAS noise is a relatively new field, there is a far greater understanding of the perception and impacts of conventional aircraft noise. Therefore, a listening experiment was designed to investigate the relationship between perception of conventional aircraft and UAS flyover noise. The experiment used mono ground plate recordings of flyovers from several different aircraft and UAS, these recordings were re-spatialised to simulate flyovers over a multi-channel loudspeaker system. Participants were then asked to rate the annoyance of the sounds through a series of A/B comparisons. Results of the experiment demonstrate a clear positive correlation between Loudness and annoyance. However, Sound Quality Metric analysis also suggests that Sharpness of the UAS sound and Tonality and/or Roughness of aircraft sounds can also be a significant factor for annoyance. The finding of this experiment may contribute to the understanding and development of exposure-response relationships for UAS noise, building upon the extensive existing evidence on human response to conventional aircraft noise.

Initial Comparison of analytical methods for noise prediction of future electric aircraft powertrains

Increasing requirements regarding greenhouse gas emissions produced in the aviation sector require new technologies for aircraft propulsion systems. A suitable technology for a more sustainable and low emission aircraft is the electrification of the powertrain. The noise emission by electric machines becomes an important contribution to the overall aircraft noise, especially since future aircraft will need high-power density electric machines that pose additional challenges regarding design and manufacturing. Electric machines with power densities of 10 kW/kg and higher are currently not available for detailed measurements, to get a first impression of the dominating noise sources of electric motors, models for predicting the noise emission are necessary. Compared to numerical methods, analytical models offer a fast estimation of the noise generated by electric motors. In this paper, analytical models for noise prediction generated by electric motors suitable for aircraft propulsion systems are presented. The most advanced model includes the calculation of the excitation forces acting on the stator due to the magnetic flux density that is coupled with a sound radiation analysis of the outer surface of the motor. By varying the basic parameters for each model under investigation, the noise radiation of a permanent magnet synchronous electric motor is assessed.

Mahua oil cake microcellulose as a performance enhancer in flax fiber composites: Mechanical strength and sound absorption analysis

Mahua oil cake microcellulose as a performance enhancer in flax fiber composites: Mechanical strength and sound absorption analysis

Unveiling the Impact of Respiratory Event-Related Hypoxia on Heart Sound Intensity During Sleep Using Novel Wearable Technology.

Cardiovascular disorders are the leading cause of mortality worldwide with obstructive sleep apnea (OSA) as the independent risk factor. Heart sounds are strong modalities to obtain clinically relevant information regarding the functioning of the heart valves and blood flow. The objective of this study was to use a small wearable device to record and investigate the changes in heart sounds during respiratory events (reduction and cessation of breathings) and their association with oxyhemoglobin desaturation (hypoxemia). Sleep assessment and tracheal respiratory and heart sounds were recorded simultaneously from 58 individuals who were suspected of having OSA. Sleep assessment was performed using in-laboratory polysomnography. Tracheal respiratory and heart sounds were recorded over the suprasternal notch using a small device with embedded microphone and accelerometer called the Patch. Heart sounds were extracted from bandpass filtered tracheal sounds using smoothed Hilbert envelope on decomposed signal. For each individual, data from 20 obstructive events during Non-Rapid Eye Movement stage-2 of sleep were randomly selected for analysis. A significant increase in heart sounds' intensities from before to after the termination of respiratory events was observed. Also, there was a significant positive correlation between the magnitude of hypoxemia and the increase in heart sounds' intensities (r>0.82, p<0.001). In addition, the changes in heart sounds were significantly correlated with heart rate and blood pressure. Our results indicate that heart sound analysis can be used as an alternative modality for assessing the cardiovascular burden of sleep apnea, which may indicate the risk of cardiovascular disorders.

Оценка уровней шума в Санкт-Петербургском метрополитене

Purpose: to assess the impact of noise levels in St. Petersburg metro cars. Identify what types of cars used on various metro lines. Determine what types of rolling stock cars used in the metro. Determine how much the service life of the cars affects the noise level in the car. Conduct measurements of noise levels inside the carriages of rolling stock of various types on various metro lines. Correlate the obtained values with the established requirements of SP 2.5.3650–20 “Sanitary rules for the operation of subways” with the values of maximum permissible noise levels in cars. Conduct an analysis of the level of noise impact on humans in the metro and identify possible measures to reduce the noise level in the metro. Methods: measurements of noise levels at various stations and trains were carried out by a special instrumental method, using a digital integrating meter and an Octave 110A sound spectrum analyzer of 1st accuracy class according to GOST R 53188.1. Results: measurements of noise levels carried out in various types of cars on various branches of the St. Petersburg metro. An analysis of the results of field measurements of noise levels for equivalent and maximum levels in the metro shows that permissible noise levels are exceeded in the “Baltiets” car on the red metro line, in car 81-717/714 on the blue metro line; in car 81-717/714 on the purple metro line; in car 81-556.2/557.2/558.2 “Neva” on the green metro line. Practical significance: identification of the obtained data show necessary measures to reduce noise in the metro. Installation of sound-absorbing materials at stations and on trains. Replacing old trains with newer ones.