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Vocal onset is the process occurring between the first detectable oscillatory glottal movement and the steady state vibration of the vocal folds. To some extent, the voice onset mirrors the voice offset. High speed imaging, photo-, electro-, flow- and ultrasonoglottography and sound analysis have been used in combinations to allow detailed qualitative insight into the phenomenon. Moreover, the instantaneous intraglottal pressure can be computed from the combined records of transglottal airflow and glottal area. A large number of vocal onsets of different types were analysed in various conditions of modal healthy phonation. Vocal folds (VF) vibration can start either from a closed glottis (hard onset) or from an open glottis (soft/breathy onset). In a soft onset, the amplitude of oscillations progressively increases over 2 to more than 30 cycles, before the first clear closed plateau is achieved. It is not possible to define whether the first movement of VF is towards medial or lateral. Hard, soft and breathy onsets can be clearly identified. Flow- and photoglottography are the most sensitive signals in detecting the first glottal movements in soft and breathy onsets. The shape of the EGG signal depends on the contact of the VF edges. The duration of the onset phase is to some extent related to VF adduction speed and peak expiratory flow. The ultrasound technique is sensitive, but lacks physiological interpretation. From the first onset-cycles on, the intraglottal pressure during the opening phase of the glottis exceeds that during the closing phase. During soft/breathy onsets with a sufficiently large number of cycles, when the vibrating mass increases, a trend appears toward a slight progressive decrease of the fundamental frequency of the oscillations, likely related to the increasing vibrating mass.

Background: Identification of the obstruction site in the upper airway may help in treatment selection for patients with sleep-disordered breathing. Because of limitations of existing techniques, there is a continuous search for more feasible methods. Snoring sound parameters were hypothesized to be po-tential predictors of the obstruction site. Therefore, this review aims to i) investigate the association between snoring sound parameters and the obstruction sites; and ii) analyze the methodology of re-ported prediction models of the obstruction sites. Methods: The literature search was conducted in PubMed, Embase.com, CENTRAL, Web of Science, and Scopus in collaboration with a medical librarian. Studies were eligible if they investigated the associa-tions between snoring sound parameters and the obstruction sites, and/or reported prediction models of the obstruction sites based on snoring sound. Results: Of the 1016 retrieved references, 28 eligible studies were included. It was found that the characteristic frequency components generated from lower-level obstructions of the upper airway were higher than those generated from upper-level obstructions. Prediction models were built mainly based on snoring sound parameters in frequency domain. The reported accuracies ranged from 60.4% to 92.2%. Conclusions: Available evidence points toward associations between the snoring sound parameters in the frequency domain and the obstruction sites in the upper airway. It is promising to build a prediction model of the obstruction sites based on snoring sound parameters and participant characteristics, but so far snoring sound analysis does not seem to be a viable diagnostic modality for treatment selection. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment, therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.

Garso sistemos naudojamos ne tik informacijos apsikeitimui ar pramogų industrijoje, bet ir technologinių įrenginių valdyme. Šiuolaikinėse garso sistemose plačiai naudojami DSP (angl. Digital Signal Processor), signalų apdorojimo procesoriai. DSP realiame laike atlieka matematinį signalų apdorojimą. Adaptavus DSP technologinių garsų analizei atsiranda galimybės projektuoti sistemas įrenginių gedimų identifikavimui. Naudojant technologinių gedimų atpažinimo sistemas, gedimus galima pastebėti ankstyvose jų stadijose ir išvengti galimų gamybos proceso trikdžių. Informacijos apie įrenginio duomenis apie jo būseną gavimui, naudojamos įvairios sistemos, jutikliai ir metodai. Tai gali būti vibraciniai jutikliai, padidėjusių variklio vibracijų nustatymui, mikrofonai pasikeitusių įrenginio skleidžiamų akustinių bangų identifikavimui, variklio srovės pokyčių analizė. Kad šią informaciją būtų galima panaudoti procesų kontrolei reikalingas jutiklių signalų apdorojimas. Tam gali būti naudojami kompiuteriai, valdikliai ar DSP. Garso signalų apdorojimui ir filtrų koeficientų apskaičiavimui parinkta programinė įranga – MATLAB. Skaitmeninių signalų procesoriaus programavimui – SigmaStudio. Tiriamojoje dalyje naudojant FIR filtrus išnagrinėti keturi technologiniai gedimai: grandiklinio transporterio su atsilaisvinusiu diržu tarp variklio ir reduktoriaus, valymo įrenginio su susidėvėjusiais guoliais, sklendės su ašmenų liečiama apsauga ir sklendės su nepasiekiama galine padėtimi gedimai. Kiekvienam iš technologinių gedimų garso įrašų pritaikyta greitoji Furjė transformacija paverčianti garso signalą iš laiko srities į dažnio sritį. Taip pat ištirtas pašalinių technologinių įrenginių poveikis įrašinėjant technologinio įrenginio garso įrašą. Tyrimas atliktas naudojant keturis technologinius įrenginius esančius 0,5 m, 3 m ir 4 m atstumais. Nustatyta, kad pašaliniai technologiniai įrenginiai neturi įtakos tiriamojo technologinio įrenginio garso įrašui. SigmaStudio aplinkoje sudaryta ištirtus technologinius gedimus aptinkanti ir indikuojanti programa. Šioje programoje naudojami FIR filtrai, signalų užlaikymo, signalų palyginimo ir signalų generavimo funkciniai blokai. Sudaryta programa naudojanti skaitmeninį signalų procesorių ADAU1701 gebanti identifikuoti grandiklinio transporterio atsilaisvinusio diržo tarp variklio ir reduktoriaus, valymo įrenginio susidėvėjusių guolių, sklendės ašmenų liečiamos apsaugos ir sklendės nepasiekiamos galinės padėties technologinius gedimus., Sound systems are used not only in information exchange or entertainment industry but also in control of technological devices. These days audio systems widely use digital signal processors. DSP calculates mathematical signal processing in real time. Adapting the DSP for technological sound analysis makes it possible to project systems for equipment failure identification. Using technological fault identification systems, the faults can be detected in their early stages preventing the stops in production processes. To gather the information about the equipment state various systems, sensors and methods are used. Those can be vibration sensors which detect motor vibration increases, microphones to detect the change of acoustic waves emitted by the equipment, analysis of motor current changes. To use this information for control of processes system first must process the sensor signal. Computers, controllers, or DSPs can be used for this. For sound signal processing and calculation of filter coefficients program MATLAB was selected. Digital signal processor programming was done by using SigmaStudio program. In the research part FIR filters were used to investigate four technological faults: chain conveyor with loose belt between motor and reductor, grain cleaning device with worn out bearings, valve with fan blade touching the protective cover and valve with inaccessible end position faults. For each sound file of the technological fault fast Fourier transform was used to convert the signal from time domain to frequency domain. The effect of foreign technological devices on the recording of the motor emitted sound was also investigated. The study was conducted using four technological devices at distances of 0,5 m, 3 m, and 4 m. It was found that external devices do not affect the sound recording of the investigated technological device. A program which identifies and indicates the technological faults of equipment was created in program SigmaStudio. In this program FIR filters, signal delay, signal comparison and signal generation function blocks were used. A program using digital signal processor ADAU1701 capable of identifying the technological failures of chain conveyor with loose belt between motor and reductor, grain cleaning device with worn out bearings, valve with fan blades touching the protective cover and valve with inaccessible end position has been developed.

Aims Recent technological advances enable diagnosing of obstructive coronary artery disease (CAD) from heart sound analysis with a high negative predictive value. However, the prognostic impact of this approach remains unknown. To investigate the prognostic value of heart sound analysis as two scores, the Acoustic-score and the CAD-score, in patients with suspected CAD which is treated according to standard of care. Methods and results Consecutive patients with angina symptoms referred for coronary computed tomography angiography (CTA) were enrolled. The Acoustic-score was developed from eight acoustic CAD-related features. This score was combined with risk factors to generate the CAD-score. A cut-off score >20 was pre-specified for both scores to indicate disease. If coronary CTA raised suspicion of obstructive CAD, patients were referred to invasive angiography and revascularized when indicated. Of 1675 enrolled patients, 1464 (87.4%) were included in this substudy. The combined primary endpoint was all-cause mortality and myocardial infarction (n = 26). Follow-up was 3.1 (2.7–3.4) years. Of patients with primary endpoints, the Acoustic-score was >20 in 25 (96%); the CAD-score was >20 in 22 (85%). In an unadjusted Cox analysis of the primary endpoints, the hazard ratio for scores >20 under current standard clinical care was 12.6 (1.7–93.2) for the Acoustic-score and 5.4 (1.9–15.7) for the CAD-score. The CAD-score contained prognostic information even after adjusting for lipid-lowering therapy initiation, stenosis at CTA, and early revascularization. Conclusion Heart sound analysis seems to carry prognostic information and may improve initial risk stratification of patients with suspected CAD. Clinicaltrials.org ID NCT02264717.

SummaryBirdsong is an important signal in mate attraction and territorial defense. Quantifying the complexity of these songs can shed light on individual fitness, sexual selection, and behavior. Several techniques have been used to quantify song complexity and be broadly categorized into diversity indices, measures of stationary probabilities, and measures of sequential variations. However, these methods are unable to account for important acoustic features like the frequency bandwidth and the variety in the shape of syllables which are an integral part of these vocal signals. This study proposes a new complexity measure that considers intra-song note variability and calculates a weighted index for birdsongs using spectral cross-correlation.We compared the previously described methods to understand the advantages and limitations based on the factors that would be affecting the complexity of songs. We developed a new method- Note Variability Index (NVI), which incorporates the spectral features of notes while quantifying complexity. This measure alleviates the need for manual annotations of notes that can be error-prone. We used Spectrogram Cross-Correlation (SPCC) to compare notes within a song and used the output values to quantify song complexity.To check for the efficacy of the new method, we generated synthetic songs to caricature extremes in song complexity and compared selected conventional complexity measures along with the NVI. We provide case-specific limitations of these methods. Additionally, to examine the efficacy of this new method in real-world scenarios, we used natural birdsongs from multiple species across the globe with varying song structures to compare conventional methods with NVI.To our knowledge, NVI is the only song complexity method that captures the variation of spectral features of notes in songs where the conventional methods fail to distinguish between similar song structures with different note types. As NVI does not need a manual classification of notes, it can be easily implemented for any type of birdsong with existing sound analysis softwares; it is very quick, avoids the possible biases in note classification, and can possibly be automated for large datasets in the future.

One of the big problems of today’s manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.

Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.

Induction motors are widely used not only in home appliances but also in industries because of their important role in electromechanical energy conversion. Overloading is among those which can shorten the operating life of those electric machines. Our research tries to use a single microphone to distinguish between full load, 10 percent overload, and 100 percent overload operations of induction motors. Three acoustic features and five classification models are evaluated to establish an overload classification system based on sound analysis. Obtained results show that this is a promising way to classify and monitor induction motor overload.

In recent years, research related to autonomous and remotely controlled ships has become a major topic in the maritime field. At this laboratory, research into the field of remote control of ship operations was established in 2015.The prototype of the remote-control system has already been completed. We are studying methods and systems to support remote control. We think it is necessary to confirm that the command of the operator who operates the ship at a remote place is executed on the ship to be operated by not only the measuring instrument but also the actual measurement (ex. Sound and vibration). Since seafarers actually use sound for engine monitoring, in this study we are studying a system that analyzes measured engine sound data and calculates the actual operating conditions. In this paper, we show the result of preliminary research to detect the feature quantity from the result of analyzing the collected data of the engine sound that is given each maneuvering command.

Computer audition (CA) has been demonstrated to be efficient in healthcare domains for speech-affecting disorders (e.g., autism spectrum, depression, or Parkinson's disease) and body sound-affecting abnormalities (e. g., abnormal bowel sounds, heart murmurs, or snore sounds). Nevertheless, CA has been underestimated in the considered data-driven technologies for fighting the COVID-19 pandemic caused by the SARS-CoV-2 coronavirus. In this light, summarise the most recent advances in CA for COVID-19 speech and/or sound analysis. While the milestones achieved are encouraging, there are yet not any solid conclusions that can be made. This comes mostly, as data is still sparse, often not sufficiently validated and lacking in systematic comparison with related diseases that affect the respiratory system. In particular, CA-based methods cannot be a standalone screening tool for SARS-CoV-2. We hope this brief overview can provide a good guidance and attract more attention from a broader artificial intelligence community., 2 pages

Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also prepare for a future outbreak which might happen due to new strains of COVID-19. Challenges and directions for the future are also provided.

Danışman: DR. ÖĞR. ÜYESİ SELVA ZEREN Yer Bilgisi: İstanbul Gelişim Üniversitesi / Lisansüstü Eğitim Enstitüsü / Odyoloji Ana Bilim Dalı / Odyoloji Bilim Dalı Konu: Kulak Burun ve Boğaz = Otorhinolaryngology (Ear-Nose-Throat) Dizin: Elektroglottografi = Electroglottograpy ; Ses = Voice ; Ses analizi = Voice analysis ; Stres = Stress ; Çalışanlar = Workers ; İtfaiye = Fire department ; İtfaiyeci = Fireman, İtfaiye erleri, yoğun iş yükü ve hayati tehlike arz edecek olaylarla bire bir karşılaşmaları nedeni ile stres düzeyi yüksek bir meslek grubudur. Stres düzeyi yüksek mesleklerde çalışanların ses kalitesinin etkilendiği bilinmektedir. Bu tez çalışmasında stres düzeyini ölçmek amacıyla ASÖ (Algılanan Stres Ölçeği) anketi kullanılmıştır. Elektroglottografi cihazı ve Laryngograph Speech Studio programı kullanılarak itfaiye erlerine ses ölçümü yapılmıştır. Araştırmanın evrenini, Avcılar ve Kıraç İtfaiye İstasyonunda çalışan 25 itfaiye eri oluşturmaktadır. Çalışmaya katılan tüm bireyler erkektir. Çalışmada elde edilen akustik analizlerde temel frekans (F0), % Jitter ve % Shimmer, MFZ parametreleri ile ASÖ puanları ve demografik anket verileri SPSS istatistik programı kullanılarak karşılaştırıldı. Çalışmamızda sigara kullanan ve kullanmayan bireyler arasında hijyen öncesi ve sonrasında yapılan pertürbasyon parametrelerine göre F0 ve %jitter parametrelerinde istatiksel olarak anlamlı bir fark gözlenmemiştir (p>0.05). Stres puanı yüksek bulunan bireylerin sadece geniş bantta F1 /İ/ ölçümünde negatif yönde anlamlı bir ilişki görülürken, diğer değerlerde anlamlı bir farklılık gözlenmemiştir. Bunun nedeni, pandemi döneminin getirdiği kısıtlamalardan dolayı çalışmaya katılan gönüllü kişi sayısının azlığı olabilir. Çalışmamıza katılan bireylerin stres düzeyleri karşılaştırıldığında ise dar ve geniş bantta shimmer /I/ harfi ölçümünde anlamlı fark bulunmuştur. Buna göre, stres düzeyi yüksek olanların dar ve geniş bantta shimmer /I/ ölçümleri, stres düzeyi düşük olanlara göre daha düşüktür. Çalışmamızın sonunda elde edilen veriler değerlendirildiğinde itfaiye erlerinin iş yükünün hafifletilmesiyle birlikte stres düzeylerinin en aza indirilmesi için terapi ve etkinlikler düzenlenmesinin faydalı olacağı düşünülmektedir. Ses kalitesinin korunması için doğru nefes alma ve sesi düzgün kullanma konusunda eğitim programları oluşturulabilir., Firefighters are under hihg level of stress because of facing with life-threatening events and heavy work load. It is known that the sound quality of employees is affected in occupations with high stress. In this thesis study, PSS (Perceived Stress Scale) questionnaire was used to measure the stress level. Electroglottography (EGG) and Laryngograph Speech Studio program sound measurements were made to firefighters. The universe of the research was Avcılar and Kıraç Fire Department. It consists of 25 firefighters working at the station. All individuals participating in the study were male. In the acoustic analyzes obtained in the study, the fundamental frequency (F0), % Jitter and % Shimmer, MFZ parameters, PSS scores and demographic survey data, compared using SPSS statistical program. In our study, the pre-hygiene and perturbation parameters made after the F0 and %jitter parameters, statistical results were obtained. No significant difference was observed (p>0.05). Individuals with a high stress score. In the broadband F1 /I/ measurement, there is a negative significant relationship, while in other values no significant difference was observed. Because of the pandemic period limitations, the number of volunteers participating in the study may be low. When the stress levels of the individuals participating in our study were compared, there were significant difference in narrow and broad bands. A significant difference was found in the shimmer /I/ letter measurement. Accordingly, the stress level is high shimmer /I/ measurements in narrow and wide bands of those with low stress levels were more is low. When the data obtained at the end of our study is evaluated, the job therapy and activities to minimize stress levels along with alleviating the burden of regulation is thought to be beneficial. Training programs can be created on how to breath correctly and use sound properly.

Information retrieval and machine learning have grown to the point of playing a leading role in all aspects of sound analysis. There are some research areas, however, in which the potential of information retrieval techniques is only now beginning to have an impact on the research community. One of these areas is musical acoustics. In this area, in fact, machine intelligence and information retrieval have been widely used only for timbral analysis of the tone produced by musical instruments, thus effectively objectifying an analysis that is traditionally considered subjective. The role of information retrieval and machine learning in the analysis of vibrational and acoustic properties of musical instruments is a more recent development. In this manuscript, we offer an overview on methodologies for vibrational, acoustic and timbral analysis, based on machine intelligence. We discuss some challenges that are emerging today and will have to be faced in the near future in order to make information retrieval and machine learning become an integral part of the analysis process in musical acoustics.

Sounds emitted by mechanisms and organisms contain information that can be used to diagnose the current state of an object and make predictions. There are known examples of the use of sound for diagnostics of pipelines, composite materials, industrial equipment. In most cases, devices with one sensor and limited functionality are used for this purpose, requiring a specialist to be in close proximity to the object under analysis. The system includes a set of digital microelectromechanical (MEMS) microphones, information from which is transmitted via Bluetooth to the mobile device. The system in a round-the-clock mode quickly registers changes in the spectrum of the sound signaland indicates possible malfunctions, damage to equipment and materials, etc. This allows preventing irreversible consequences. In this system, it is expedient to use digital MEMS microphones due to their low power consumption and low sensitivity to environmental influences, which is an important factor when deploying a remote monitoring system of industrial equipment., Звуки, издаваемые механизмами и организмами, содержат информацию, которую можно использовать для диагностики текущего состояния объекта и прогноза на будущее. Известны примеры использования звука для диагностики трубопроводов, композитных материалов, промышленного оборудования. В большинстве случаев для этой цели применяются устройства с одним датчиком и ограниченным функционалом, требующие нахождения специалиста в непосредственной близости к исследуемому объекту. Предлагается система для дистанционного мониторинга, включающая в себя набор датчиков-микрофонов, информация с которых передается посредством Bluetooth на мобильное устройство. Система в круглосуточном режиме оперативно регистрирует изменения в спектре звукового сигнала, свидетельствующие о возможных неисправностях, повреждении оборудования и материалов и т. д., что позволяет предупредить необратимые последствия. В данной системе целесообразно применять цифровые МЭМС-микрофоны вследствие их низкого энергопотребления и низкой чувствительности к воздействию окружающей среды, что является важным фактором при развертывании системы дистанционного мониторинга промышленного оборудования.

This study aimed to verify the effect of anesthetic with ophthalmic eye drops tetracaine hydrochloride 1% and phenylephrine hydrochloride 0.1% in frenotomy in ankyloglossia in infants. The sample consisted of 24 children aged 2 to 6 months, indicated for frenotomy from February to August 2019, performed by the pediatric dentistry team of the Health Unit Service (SUS), in the city of Gurupi, Tocantins State, Brazil. We chose to compare the ophthalmic anesthetic with two anesthetics commonly used in frenotomies. The children were evaluated by sound recording at two moments of the surgical act by a mobile device with a calibrated "decibel meter" application. The mean intensities of infant crying in surgery with phenylephrine/tetracaine topical were significantly lower than the mean intensities when lidocaine injection (P=0.0096) was used, but the same statistical procedure applied, comparing topical Benzocaine versus lidocaine injection, showed no significant difference (P=0.35). The methods of noninvasive topical applications were better than invasive ones, the sound analysis showed that topical application of ophthalmic eye drops of tetracaine/phenylephrine proved to be the best analgesia in frenotomies.

Since the historical article by Forgacs in 1978, many studies have clarified the changes of lung sounds due to airway narrowing as well as the mechanism of genesis of these sounds. Studies using bronchoprovocation have shown that an increase of the frequency and/or intensity of lung sounds was a common finding of airway narrowing and correlated well with lung function in bronchial asthma. Bronchoprovocation studies also showed that wheezing may not be as sensitive as changes in basic lung sounds in acute airway narrowing in adult asthmatics. In lung sound analysis, narrow airways cause an increase in the frequency of breath sounds and lung sound intensity, implying when the patient has higher than normal breath sounds, i.e., bronchial sounds, he or she may have airway narrowing. Recent studies reported that this increase of breath sounds suggested worsening of airway inflammation in rather stable patients with bronchial asthma. As it is difficult to detect subtle changes in lung sounds by auscultation alone, automated sound analysis will be expected.

The left ventricular assist device (LVAD) has emerged as a bridge or alternative to heart transplant in individuals with advanced heart failure. However, the LVAD recipient population currently faces high rehospitalization rates. Remote analysis of precordial sounds in LVAD recipients may improve aftercare through early detection of complications. Prior work on analyses of precordial sounds in LVAD recipients focused on identifying pump thrombosis. Here, we focus on analyzing intrinsic precordial sounds to provide insight into intrinsic cardiac function. We analyzed a dataset of patient-acquired recordings of precordial sounds in LVAD recipients. We developed a signal processing pipeline to separate LVAD-generated sounds from other precordial sounds, making heart sound analysis in LVAD recipients feasible. Unsupervised clustering of features extracted from LVAD-mitigated sounds revealed subgroups of subjects possessing heart sounds with distinct frequency characteristics. The results provide preliminary evidence for the potential utility of exploring heart sound analysis in LVAD recipients for remote monitoring.

Traffic monitoring is a quiddity to understand occurrence of road transportation, for example, route decision in each festival, driving behavior, passerby quantity, traffic density and road contamination from road traffic, especially the accidents. Accident randomly happen in anytime and it dominates the high portion cause of death in each year. Accident is a cause of traffic jam. The assistance may come lately because no one cannot be noticed this event immediately. The traffic monitoring by applying sound analysis processing for accident sound with Convolutional Neural Network (CNNs) can significantly improve efficiency of traffic monitoring.

Passive acoustic monitoring of soundscapes and biodiversity produces vast amounts of audio recordings. However, the management of these raw data presents technical challenges and their analysis suffers from bottlenecks. A multitude of software solutions exist, but none can perform all the data processing needed by ecologists for analysing large acoustic data sets. The field of ecoacoustics needs a software tool that is free, evolving, and accessible. We take a step in that direction and present BioSounds: an open-source, online platform for ecoacoustics designed by ecologists and built by software engineers. Biosounds can be used for archiving and sharing recordings, manually creating and reviewing annotations of sonant animals in soundscapes, analysing audio in time and frequency, and storing reference recordings for different taxa. We present its features and structure, and compare it with similar software. We describe its operation mode and the workflow for typical use cases such as the analysis of bird and bat communities sampled in soundscape recordings. BioSounds is available from: https://github.com/nperezg/biosounds Open-Access-Publikationsfonds 2020 peerReviewed

Recent years have seen a worsening in the decline of honey bees (Apis mellifera L.) colonies. This phenomenon has sparked a great amount of attention regarding the need for intense bee hive monitoring, in order to identify possible causes, and design corresponding countermeasures. Honey bees have a key role in pollination services of both cultivated and spontaneous flora, and the increase in bee mortality could lead to an ecological and economical damage. Despite many smart monitoring systems for honey bees and bee hives, relying on different sensors and measured quantities, have been proposed over the years, the most promising ones are based on sound analysis. Sounds are used by the bees to communicate within the hive, and their analysis can reveal useful information to understand the colony health status and to detect sudden variations, just by using a simple microphone and an acquisition system. The work here presented aims to provide a review of the most interesting approaches proposed over the years for honey bees sound analysis and the type of knowledge about bees that can be extracted from sounds.

Transmission of real-time data is strongly increasing due to remote processing of sensor data, among other things. A route to meet this demand is adaptive sensing, in which sensors acquire only relevant information using pre-processing at sensor level. We present here adaptive acoustic sensors based on mechanical oscillators with integrated sensing and actuation. Their dynamics are shifted into a nonlinear regime using feedback or coupling. This enhances dynamic range, frequency resolution and signal-to-noise ratio. Combining tunable sensing properties with sound analysis could enable acquiring of only relevant information rather than extracting this from irrelevant data by post-processing., Comment: accepted for publication in 2020 IEEE International Symposium on Circuits and Systems (ISCAS)

The report presents the study of recognition of stress states through Multilayer Backpropagation Neural Networks (MBNN) based on extracted sound indicators at speech signal analysis. Experimental neural architectures with tangent-sigmoid and logarithmic-sigmoid functions in the two hidden layers with different initial activation were tested. The models with initial logarithmic-sigmoid type, where the obtained maximum accuracy is 75.00%, are defined as inapplicable. In MBNNs with linear and tangential-sigmoidal functions in the initial layers, significantly better adequacy was found as the highest levels of the indicator reached 100.00%.

V tej diplomski nalogi opisujemo izdelavo aplikacije za uglaševanje klarineta z uporabo računalnika Raspberry Pi. Spoznamo uglaševanje klarineta, uporabo zvočne analize nad realnočasovnim zvokom, uporabo priključkov GPIO za povezovanje zunanjih naprav, prenos podatkov po protokolu UART in zajem zvoka po protokolu I2S. Aplikacija je napisana v programskem jeziku Python, njen vizualni izgled pa v jeziku C#. Aplikacija za uglaševanje klarineta je namenjena domači uporabi, saj za delovanje potrebuje povezavo z osebnim računalnikom, ne pa internetne povezave. V primeru, ko naše glasbilo ni uglašeno, nam aplikacija poda informacijo o stanju intonacije našega glasbila in jo primerja z želenim tonom. Prav tako lahko vidimo prikaz dejanske frekvence, zaigrane na glasbilo. Podani so tudi nasveti za uglasitev. In this thesis we describe an application for clarinet tuning on a Raspberry pi computer. We get to know the theory behind the clarinet tuning, realtime sound analysis, GPIO inputs for connecting external devices, data transfer over UART protocol and sound capture over I2S protocol. Application is implemented in Python programming language whereas its user interface is coded in C# language. Application for clarinet tuning is meant for home use, as it needs a personal computer connection but it does not need an internet connection. In a scenario where our instrument is out of tune, the application provides us with the intonation of the instrument and compares it to the desired tone. The application displays the actual frequency of played tone as well and it also provides tips for further tuning.

Knees, hip, and other human joints generate noise and vibration while they move. The vibration and sound pattern is characteristic not only for the type of joint but also for the condition. The pattern vary due to abrasion, damage, injury, and other causes. Therefore, the vibration and sound analysis, also known as vibroarthrography (VAG), provides information and possible conclusions about the joint condition, age and health state. The analysis of the pattern is very sophisticated and complex and so approaches of machine learning techniques were applied before. In this paper, we are using convolutional neural networks for the analysis of vibroarthrographic signals and compare the results with already known machine learning techniques.

We present a new method for the discrimination of explosive cough events based on a combination of spectral and pitch-related features. The method was tested on 16 distinct partitions of a database with 9 patients. After a pre-processing stage where non-relevant segments were discarded, we have extracted eight features from each of the other segments and have fed them to the classifiers. Four types of algorithms were implemented to classify the events, with Bayesian classifiers achieving the best performance. Preliminary results showed that performance increased when the analysis was performed on individual subjects and when specific sensor locations were chosen. These results demonstrate that personalizing the analysis is a promising approach and shed some light on where to put sensors when automatic analysis is performed in the future. published

As a key equipment for production and transportation, conveyor belts are widely used in the coal mining industry. Once the conveyor belt longitudinal tear occurs, it will seriously affect the production and even cause personal injury. Therefore, the longitudinal tear detection of the conveyor belt is extremely important. In this paper, the sound detection is introduced into the longitudinal tear detection of the conveyor belt, and an audio-visual detection method for conveyor belt longitudinal tear is proposed. Camera and microphone array are used to collect the image and sound signals of conveyor belt, and the conveyor belt is detected from both image and sound, respectively. Then the image and sound analysis results are combined to comprehensively judge the status of the conveyor belt. The experimental results show that the audio-visual detection method can accurately identify the normal, abnormal, and longitudinal tear of the conveyor belt. The detection accuracy is over 86.72% and the sensitivity of longitudinal tear detection is greater than 92.59%. The proposed audio-visual detection method is verified to meet the requirements of longitudinal tear detection of coal mining industry conveyor belts.

Economic pressures continue to mount on modern-day livestock farmers, forcing them to increase herds sizes in order to be commercially viable. The natural consequence of this is to drive the farmer and the animal further apart. However, closer attention to the animal not only positively impacts animal welfare and health but can also increase the capacity of the farmer to achieve a more sustainable production. State-of-the-art precision livestock farming (PLF) technology is one such means of bringing the animals closer to the farmer in the facing of expanding systems. Contrary to some current opinions, it can offer an alternative philosophy to 'farming by numbers'. This review addresses the key technology-oriented approaches to monitor animals and demonstrates how image and sound analyses can be used to build 'digital representations' of animals by giving an overview of some of the core concepts of PLF tool development and value discovery during PLF implementation. The key to developing such a representation is by measuring important behaviours and events in the livestock buildings. The application of image and sound can realise more advanced applications and has enormous potential in the industry. In the end, the importance lies in the accuracy of the developed PLF applications in the commercial farming system as this will also make the farmer embrace the technological development and ensure progress within the PLF field in favour of the livestock animals and their well-being. ispartof: ANIMAL vol:13 issue:12 pages:3009-3017 ispartof: location:England status: published

The severity of obstructive sleep apnoea (OSA) is diagnosed with polysomnography (PSG), during which patients are monitored by over 20 physiological sensors overnight. These sensors often bother patients and may affect patients&rsquo, sleep and OSA. This study aimed to investigate a method for analyzing patient snore sounds to detect the severity of OSA. Using a microphone placed at the patient&rsquo, s bedside, the snoring and breathing sounds of 22 participants were recorded while they simultaneously underwent PSG. We examined some features from the snoring and breathing sounds and examined the correlation between these features and the snore-specific apnoea-hypopnea index (ssAHI), defined as the number of apnoea and hypopnea events during the hour before a snore episode. Statistical analyses revealed that the ssAHI was positively correlated with the Mel frequency cepstral coefficients (MFCC) and volume information (VI). Based on clustering results, mild snore sound episodes and snore sound episodes from mild OSA patients were mainly classified into cluster 1. The results of clustering severe snore sound episodes and snore sound episodes from severe OSA patients were mainly classified into cluster 2. The features of snoring sounds that we identified have the potential to detect the severity of OSA.