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Exploring and understanding emotions when treating children with autism spectrum disorders (ASD) is of fundamental importance for both therapists and children themselves. People with ASD often use specific communication methods, which may make it difficult to express their feelings using conventional verbal and non-verbal techniques. Therefore, a deep analysis of the subtle aspects of their speech, intonation, rhythm, and other auditory forms of expression can provide valuable clues about their emotions, needs, and reactions to therapeutic activities. Children's emotional responses can manifest in many ways, including voice modulation, which can signal anger, sadness, or happiness. Therapists can better interpret the child's intentions and reactions to surrounding stimuli using voice analysis methods. For example, a flat tone of voice may indicate apathy or anxiety, while a high volume may indicate excitement or tension. Additionally, various unconventional sounds a child makes can provide valuable information about their emotional and mental state. To capture these signals, therapists can use advanced equipment for recording and analyzing sounds and robots equipped with image and sound recording functions, which help in therapy.

Passive acoustic monitoring of soundscapes and biodiversity produces vast amounts of audio recordings, but the management and analyses of these raw data present technical challenges. A multitude of software solutions exist, but none can fulfil all purposes required for the management, processing, navigation, and analysis of acoustic data. The field of ecoacoustics needs a software tool that is free, evolving, and accessible. We take a step in that direction and present ecoSound-web: an open-source, online platform for ecoacoustics designed and built by ecologists and software engineers. ecoSound-web can be used for storing, re-sampling, organising, analysing, and sharing soundscape recording or metadata projects. Specifically, it allows manual annotation of soniferous animals and soundscape components, automatic annotation with deep-learning models for all birds and for UK bat species, peer-reviewing annotations, analysing audio in time and frequency dimensions, computing alpha acoustic indices, and providing reference sound libraries for different taxa. We present ecoSound-web’s structure and features, and describe its operation for typical use cases such as sampling bird and bat communities, using a primate call library, and the analysis of soundscape components and acoustic indices. ecoSound-web is available from: https://github.com/ecomontec/ecoSound-web

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3–5% better accuracy results. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring of soundscapes and biodiversity produces vast amounts of audio recordings, but the management and analyses of these raw data present technical challenges. A multitude of software solutions exist, but none can fulfil all purposes required for the management, processing, navigation, and analysis of acoustic data. The field of ecoacoustics needs a software tool that is free, evolving, and accessible. We take a step in that direction and present ecoSound-web: an open-source, online platform for ecoacoustics designed and built by ecologists and software engineers. ecoSound-web can be used for storing, re-sampling, organising, analysing, and sharing soundscape recording or metadata projects. Specifically, it allows manual annotation of soniferous animals and soundscape components, automatic annotation with deep-learning models for all birds and for UK bat species, peer-reviewing annotations, analysing audio in time and frequency dimensions, computing alpha acoustic indices, and providing reference sound libraries for different taxa. We present ecoSound-web’s structure and features, and describe its operation for typical use cases such as sampling bird and bat communities, using a primate call library, and the analysis of soundscape components and acoustic indices. ecoSound-web is available from: https://github.com/ecomontec/ecoSound-web

Detecting instruments in a music signal is often used in database indexing, song annotation, and creating applications for musicians and music producers. Therefore, effective methods that automatically solve this issue need to be created. In this paper, the mentioned task is solved using mel-frequency cepstral coefficients (MFCC) and various architectures of artificial neural networks. The authors' contribution to the development of automatic instrument detection covers the methods used, particularly the neural network architectures and the voting committees created. All these methods were evaluated, and the results are presented and discussed in the paper. The proposed automatic instrument detection methods show that the best classification quality was obtained for an extensive model, which is the so-called committee of voting classifiers. [ABSTRACT FROM AUTHOR]

In the paper, personalized results are presented in the methodology for monitoring information security based on voice authentication. Integration of sound preprocessing and Machine Learning techniques for feature extraction, training, and validation of classification models has been implemented. The objects of research are staked mixed-test voice profiles. Classifies were selected with quantitative evaluation under a threshold of 90.00% by Naive Bayes and Discriminant Analysis. Significantly improved accuracy to approximate levels of 96.0% was established at Decision Tree synthesis. Strongly satisfactory performance indices were reached at the diagnosis of voice profiles using Feed-Forward and Probabilistic Neural Networks, respectively, 98.00% and 100.00%. [ABSTRACT FROM AUTHOR]

Evening Grosbeak (Coccothraustes vespertinus) is a species of North American Fringillid finch thought to be one of the fastest declining songbirds across North America. It has been divided into five groups, potentially distinct lineages, based in part on structure of their flight calls. The primary flight calls of each type exhibit structural variation that has not been described and the degree to which that variation might lead to identification errors has not been quantified. We describe the variation in call structure of type 1 Evening Grosbeaks recorded at a spring migratory stopover site (Corvallis, Oregon) and nearby areas in the Pacific Northwest, USA. We recorded grosbeaks weekly from April through early June 2023. We reviewed more than 10,000 recorded call notes to characterize the variety of calls and their configurations. We found a high diversity of call notes including at least 11 recurring readily identifiable variants of the primary flight calls, all of which were attributed to individuals thought to be type 1 birds. Geographically, the nearest neighbors of type 1 Evening Grosbeaks are type 2s, which have uncommonly been recorded in our study area but whose breeding range appears to overlap that of type 1 in southern Oregon. We quantified recordings of type 2 flight calls and compared them with type 1 flight calls, finding that linear discriminant function analyses correctly identified >95% of recordings to type. Inclusion of a metric of asymmetry in call shape improved correct classification to 98.5%. We also found that the other dominant calls given by both types, buzzy trills, could be identified correctly to type with a high level of confidence. The sufficiently different characteristics of flight calls and trills indicate that types 1 and 2 are identifiable spectrographically in most cases, providing confidence that the geographic distribution and migratory movements of call-type populations, despite being essentially identical in plumage, can be documented effectively by characteristics of call notes. [ABSTRACT FROM AUTHOR]

This study investigates the combination of audio and image data to classify road conditions, particularly focusing on loose gravel scenarios. The dataset underwent binary categorisation, comprising audio segments capturing gravel sounds and corresponding images. Early feature fusion, utilising a pre-trained Very Deep Convolutional Networks 19 (VGG19) and Principal component analysis (PCA), improved the accuracy of the Random Forest classifier, surpassing other models in accuracy, precision, recall, and F1-score. Late fusion, involving decision-level processing with logical disjunction and conjunction gates (AND and OR) in combination with individual classifiers for images and audio based on Densely Connected Convolutional Networks 121 (DenseNet121), demonstrated notable performance, especially with the OR gate, achieving 97 % accuracy. The late fusion method enhances adaptability by compensating for limitations in one modality with information from the other. Adapting maintenance based on identified road conditions minimises unnecessary environmental impact. This method can help to identify loose gravel on gravel roads, substantially improving road safety and implementing a precise maintenance strategy through a data-driven approach.

In the paper, personalized results are presented in the methodology for monitoring information security based on voice authentication. Integration of sound preprocessing and Machine Learning techniques for feature extraction, training, and validation of classification models has been implemented. The objects of research are staked mixed-test voice profiles. Classifies were selected with quantitative evaluation under a threshold of 90.00% by Naive Bayes and Discriminant Analysis. Significantly improved accuracy to approximate levels of 96.0% was established at Decision Tree synthesis. Strongly satisfactory performance indices were reached at the diagnosis of voice profiles using Feed-Forward and Probabilistic Neural Networks, respectively, 98.00% and 100.00%.

Norske folkemusikkarkiver står foran utfordringer vedrørende å gjøre sine samlinger tilgjengelige på nett når feltopptak faller i det fri etter 50 år. Denne artikkelen beskriver et forsøk på å tilrettelegge brukskopier i form av lydfiler slik at brukerne kan lytte til enkeltopptak i en nettpresentasjon av et arkivs innhold. Lydklassifiseringsteknologi er i stand til å finne og markere starttidspunkt på enkeltmelodier og spare arkivpersonalet for mange timers manuelt arbeid. Mirage-prosjektet på UiOs RITMO-senter har utviklet et grensesnitt for et slikt verktøy for Nasjonalbibliotekets folkemusikkarkiv og dets nettkatalog WebbFIOL. Løsningen vil kunne tas i bruk av alle som står overfor liknende utfordringer.

Evening Grosbeak (Coccothraustes vespertinus) is a species of North American Fringillid finch thought to be one of the fastest declining songbirds across North America. It has been divided into five groups, potentially distinct lineages, based in part on structure of their flight calls. The primary flight calls of each type exhibit structural variation that has not been described and the degree to which that variation might lead to identification errors has not been quantified. We describe the variation in call structure of type 1 Evening Grosbeaks recorded at a spring migratory stopover site (Corvallis, Oregon) and nearby areas in the Pacific Northwest, USA. We recorded grosbeaks weekly from April through early June 2023. We reviewed more than 10,000 recorded call notes to characterize the variety of calls and their configurations. We found a high diversity of call notes including at least 11 recurring readily identifiable variants of the primary flight calls, all of which were attributed to individuals thought to be type 1 birds. Geographically, the nearest neighbors of type 1 Evening Grosbeaks are type 2s, which have uncommonly been recorded in our study area but whose breeding range appears to overlap that of type 1 in southern Oregon. We quantified recordings of type 2 flight calls and compared them with type 1 flight calls, finding that linear discriminant function analyses correctly identified >95% of recordings to type. Inclusion of a metric of asymmetry in call shape improved correct classification to 98.5%. We also found that the other dominant calls given by both types, buzzy trills, could be identified correctly to type with a high level of confidence. The sufficiently different characteristics of flight calls and trills indicate that types 1 and 2 are identifiable spectrographically in most cases, providing confidence that the geographic distribution and migratory movements of call-type populations, despite being essentially identical in plumage, can be documented effectively by characteristics of call notes.

The Neretva dwarf goby Orsinigobius croaticus (Gobiiformes, Gobionellidae) is an endemic fish native to the freshwaters of the Adriatic Basin in Croatia and Bosnia and Herzegovina, a Mediterranean Biodiversity Hotspot. Due to its limited distribution range, specific karst habitat and endangered status, laboratory studies on reproductive biology are scarce but crucial. Herein, we investigated the sound production and acoustic behaviour of the endangered O. croaticus during reproductive intersexual laboratory encounters, utilising an interdisciplinary approach. We also performed dissections and micro‐computed tomography (μCT) scanning of the pectoral girdle to explore its potential involvement in sound production. Finally, comparative acoustic analysis was conducted on sounds produced by previously recorded soniferous sand gobies to investigate whether acoustic features are species‐specific. The endemic O. croaticus is a soniferous species. Males of this species emit pulsatile sounds composed of a variable number of short (~15 ms) consecutive pulses when interacting with females, usually during the pre‐spawning phase in the nest, but also during courtship outside the nest. Pulsatile sounds were low‐frequency and short pulse trains (~140 Hz, <1000 ms). Male visual behaviour rate was higher when co‐occurring with sounds and females entered the male's nest significantly more frequently when sounds were present. Characteristic body movements accompanied male sound production, such as head thrust and fin spreading. Furthermore, μCT scans and dissections suggest that O. croaticus shares certain anatomical similarities of the pectoral girdle (i.e. osseous elements and arrangement of levator pectoralis muscles) to previously studied sand gobies that could be involved in sound production. Multivariate comparisons, using sounds produced by eight soniferous European sand gobies, effectively distinguished soniferous (and sympatric) species based on their acoustic properties. However, the discrimination success decreased when temperature‐dependent features (sound duration and pulse repetition rate) were excluded from the analysis. Therefore, we suggest both spectral and temporal features are important for the acoustic differentiation of sand gobies. [ABSTRACT FROM AUTHOR]

Detecting instruments in a music signal is often used in database indexing, song annotation, and creating applications for musicians and music producers. Therefore, effective methods that automatically solve this issue need to be created. In this paper, the mentioned task is solved using mel-frequency cepstral coefficients (MFCC) and various architectures of artificial neural networks. The authors’ contribution to the development of automatic instrument detection covers the methods used, particularly the neural network architectures and the voting committees created. All these methods were evaluated, and the results are presented and discussed in the paper. The proposed automatic instrument detection methods show that the best classification quality was obtained for an extensive model, which is the so-called committee of voting classifiers.

Abstract The Neretva dwarf goby Orsinigobius croaticus (Gobiiformes, Gobionellidae) is an endemic fish native to the freshwaters of the Adriatic Basin in Croatia and Bosnia and Herzegovina, a Mediterranean Biodiversity Hotspot. Due to its limited distribution range, specific karst habitat and endangered status, laboratory studies on reproductive biology are scarce but crucial. Herein, we investigated the sound production and acoustic behaviour of the endangered O. croaticus during reproductive intersexual laboratory encounters, utilising an interdisciplinary approach. We also performed dissections and micro‐computed tomography (μCT) scanning of the pectoral girdle to explore its potential involvement in sound production. Finally, comparative acoustic analysis was conducted on sounds produced by previously recorded soniferous sand gobies to investigate whether acoustic features are species‐specific. The endemic O. croaticus is a soniferous species. Males of this species emit pulsatile sounds composed of a variable number of short (~15 ms) consecutive pulses when interacting with females, usually during the pre‐spawning phase in the nest, but also during courtship outside the nest. Pulsatile sounds were low‐frequency and short pulse trains (~140 Hz,

Engines are prone to various types of faults, and it is crucial to detect and indeed classify them accurately. However, manual fault type detection is time‐consuming and error‐prone. Automated fault type detection promises to reduce inter‐ and intra‐observer variability while ensuring time invariant attention during the observation duration. We have proposed an automated fault‐type detection model based on sound signals to realize these advantageous properties. We have named the detection model prism pattern network (PrismPatNet) to reflect the fact that our design incorporates a novel feature extraction algorithm that was inspired by a 3D prism shape. Our prism pattern model achieves high accuracy with low‐computational complexity. It consists of three main phases: (i) prism pattern inspired multilevel feature generation and maximum pooling operator, (ii) feature ranking and feature selection using neighbourhood component analysis (NCA), and (iii) support vector machine (SVM) based classification. The maximum pooling operator decomposes the sound signal into six levels. The proposed prism pattern algorithm extracts parameter values from both the signal itself and its decompositions. The generated parameter values are merged and fed to the NCA algorithm, which extracts 512 features from that input. The resulting feature vectors are passed on to the SVM classifier, which labels the input as belonging to 1 of 27 classes. We have validated our model with a newly collected dataset containing the sound of (1) a normal engine and (2) 26 different types of engine faults. Our model reached an accuracy of 99.19% and 98.75% using 80:20 hold‐out validation and 10‐fold cross‐validation, respectively. Compared with previous studies, our model achieved the highest overall classification accuracy even though our model was tasked with identifying significantly more fault classes. This performance indicates that our PrismPatNet model is ready to be installed in real‐world applications. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The purpose of this study was to analyze the scraping sounds generated during revision knee replacement surgeries to discriminate between the inner cortical bone and the cement, with the goal of minimizing bone removal and increasing the structural integrity of the revision. Methods: We prepared seven porcine femurs by partially filling them with bone cement, and recorded scraping sounds produced by a surgical scraping tool. We used a hierarchical machine learning approach to first detect a contact and then classify it as either bone or cement. This approach was based on a Support Vector Machine learning algorithm that was fed with temporal and spectral features of the sounds. A Leave-One-Bone-Out validation method was used to assess the performance of the proposed method. Results: The average recall for the noncontact, bone, and cement classes was 98%, 75%, and 72%, respectively. The corresponding precision for the respective classes was 99%, 67%, and 61%. Conclusion: The scraping sound that is generated during revision replacement surgeries carries significant information about the material that is being scraped. Such information can be extracted using a supervised machine learning algorithm. The scraping sound produced during revision replacement procedures can potentially be used to enhance cement removal during knee revision surgery. Future work will assess whether such monitoring can increase the structural integrity of the revision. [ABSTRACT FROM AUTHOR]

This paper presents a machine-learning approach for detecting swarming events. Three different classification algorithms are tested: The k-Nearest Neighbors algorithm (k-NN) and Support Vector Machine (SVM), and a newly proposed by the authors, U-Net Convolutional Neural Network (CNN), developed for biomedical image segmentation. Next, the authors present their experimental scenario of collecting audio data of swarming and non-swarming events and evaluating the results from the k-NN and SVM classifiers and their proposed CNN algorithm. Finally, the authors compare these three methods and present the cross-comparison results of the optimal method for early and late/close-to-the-event detection of swarming.

This paper proposes an AI‐based multimodal architecture for anomaly detection in a video footage of a smart carrier which is used for monitoring alignment status, grinding of rollers and vibration of a carrier for manufacturing line of OLED (Organic Light Emitting Diodes) panels. The proposed multimodal architecture has a two‐stream structure. The first stream extracts a candidate anomaly region in image frames sampled from the video by DL‐based segmentation. Thereafter, the candidate region is determined whether it is a true failure or not by comparing the region with a reference image which was previously obtained in a normal setup status. The second stream calculates a dense optical flow with sparse areas using consecutive video frames. By observing change between (n)th, reference, (n‐3)th and (n‐7)th images, it is determined with sound analysis whether the smart carrier experiences vibration during movement. The overall detection rate of three anomalies is 97%, and processing takes only 40% of total video time. [ABSTRACT FROM AUTHOR]

Simple Summary: Dairy calves' welfare is rapidly gaining long-deserved attention from science and dairy farmers' communities. However, the elevated morbidity and mortality rates referred to in the literature reflect that there are still major problems in calves' husbandry despite the advances already made in recent years. The development of technologies may assist the traditional time-consuming welfare evaluations and improve calves' health and welfare on dairy farms. This review presents the state-of-the-art of technological advances related to dairy calves' management and welfare. Precision livestock farming (PLF) research is rapidly increasing and has improved farmers' quality of life, animal welfare, and production efficiency. PLF research in dairy calves is still relatively recent but has grown in the last few years. Automatic milk feeding systems (AMFS) and 3D accelerometers have been the most extensively used technologies in dairy calves. However, other technologies have been emerging in dairy calves' research, such as infrared thermography (IRT), 3D cameras, ruminal bolus, and sound analysis systems, which have not been properly validated and reviewed in the scientific literature. Thus, with this review, we aimed to analyse the state-of-the-art of technological applications in calves, focusing on dairy calves. Most of the research is focused on technology to detect and predict calves' health problems and monitor pain indicators. Feeding and lying behaviours have sometimes been associated with health and welfare levels. However, a consensus opinion is still unclear since other factors, such as milk allowance, can affect these behaviours differently. Research that employed a multi-technology approach showed better results than research focusing on only a single technique. Integrating and automating different technologies with machine learning algorithms can offer more scientific knowledge and potentially help the farmers improve calves' health, performance, and welfare, if commercial applications are available, which, from the authors' knowledge, are not at the moment. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring of soundscapes and biodiversity produces vast amounts of audio recordings, but the management and analysis of these raw data present technical challenges. A multitude of software solutions exist, but none can fulfil all purposes required for the management, processing, navigation, analysis, and dissemination of acoustic data. The field of ecoacoustics needs a software tool that is free, evolving, and accessible. We take a step in that direction and present ecoSound-web: an open-source, online platform for ecoacoustics designed and built by ecologists and software engineers. ecoSound-web can be used for storing, organising, and sharing soundscape projects, manually creating and peer-reviewing annotations of soniferous animals and phonies, analysing audio in time and frequency, computing alpha acoustic indices, and providing reference sound libraries for different taxa. We present ecoSound-web’s features, structure, and compare it with similar software. We describe its operation mode and the workflow for typical use cases such as the sampling of bird and bat communities, the use of a primate call library, and the analysis of phonies and acoustic indices. ecoSound-web is available from: https://github.com/ecomontec/ecoSound-web

Pig welfare is closely related to the economical production of pig farms. With regard to pig welfare assessment, pig sounds are significant indicators, which can reflect the quality of the barn environment, the physical condition and the health of pigs. Therefore, pig sound analysis is of high priority and necessary. In this review, the relationship between pig sound and welfare was analyzed. Three kinds of pig sounds are closely related to pig welfare, including coughs, screams, and grunts. Subsequently, both wearable and non-contact sensors were briefly described in two aspects of advantages and disadvantages. Based on the advantages and feasibility of microphone sensors in contactless way, the existing techniques for processing pig sounds were elaborated and evaluated for further in-depth research from three aspects: sound recording and labeling, feature extraction, and sound classification. Finally, the challenges and opportunities of pig sound research were discussed for the ultimate purpose of precision livestock farming (PLF) in four ways: concerning sound monitoring technologies, individual pig welfare monitoring, commercial applications and pig farmers. In summary, it was found that most of the current researches on pig sound recognition tasks focused on the selection of classifiers and algorithm improvement, while fewer research was conducted on sound labeling and feature extraction. Meanwhile, pig sound recognition faces some challenging problems, involving the difficulty in obtaining the audio data from different pig growth stages and verifying the developed algorithms in a variety of pig farms. Overall, it is suggested that technologies involved in the automatic identification process should be explored in depth. In the future, strengthen cooperation among cross-disciplinary experts to promote the development and application of PLF is also nessary.

According to statistics, the number of hearing-impaired persons among the disabled in Korea accounts for 27% of all persons with disabilities. However, there is insufficient support for the deaf and hard of hearing's protective devices and life aids compared to the large number. In particular, the hearing impaired misses much information obtained through sound and causes inconvenience in daily life. Therefore, in this paper, we propose a method to relieve the discomfort in the daily life of the hearing impaired. It analyzes sounds that can occur frequently and must be recognized in daily life and guide them to the hearing impaired through applications and vibration bracelets. Sound analysis was learned by using deep learning by converting sounds that often occur in daily life into the Mel-Spectrogram. The sound that actually occurs is recorded through the application, and then it is identified based on the learning result. According to the identification result, predefined alarms and vibrations are provided differently so that the hearing impaired can easily recognize it. As a result of the recognition of the four major sounds occurring in real life in the experiment, the performance showed an average of 85% and an average of 80% of the classification rate for mixed sounds. It was confirmed that the proposed method can be applied to real-life through experiments. Through the proposed method, the quality of life can be improved by allowing the hearing impaired to recognize and respond to sounds that are essential in daily life.

Passive acoustic monitoring of soundscapes and biodiversity produces vast amounts of audio recordings, but the management and analyses of these raw data present technical challenges. A multitude of software solutions exist, but none can fulfil all purposes required for the management, processing, navigation, analysis, and dissemination of acoustic data. The field of ecoacoustics needs a software tool that is free, evolving, and accessible. We take a step in that direction and present ecoSound-web: an open-source, online platform for ecoacoustics designed and built by ecologists and software engineers. ecoSound-web can be used for storing, organising, and sharing soundscape projects, manually creating and peer-reviewing annotations of soniferous animals and phonies, analysing audio in time and frequency, computing alpha acoustic indices, and providing reference sound libraries for different taxa. We present ecoSound-web’s features, structure, and compare it with similar software. We describe its operation mode and the workflow for typical use cases such as the sampling of bird and bat communities, the use of a primate call library, and the analysis of phonies and acoustic indices. ecoSound-web is available from: https://github.com/ecomontec/ecoSound-web

Bowel sound (BS) is receiving more attention as an indicator of gut health since it can be acquired non-invasively. Current gut health diagnostic tests require special devices that are limited to hospital settings. This study aimed to develop a prototype smartphone application that can record BS using built-in microphones and automatically analyze the sounds. Using smartphones, we collected BSs from 100 participants (age 37.6 ± 9.7). During screening and annotation, we obtained 5929 BS segments. Based on the annotated recordings, we developed and compared two BS recognition models: CNN and LSTM. Our CNN model could detect BSs with an accuracy of 88.9% andan F measure of 72.3% using cross evaluation, thus displaying better performance than the LSTM model (82.4% accuracy and 65.8% F measure using cross validation). Furthermore, the BS to sound interval, which indicates a bowel motility, predicted by the CNN model correlated to over 98% with manual labels. Using built-in smartphone microphones, we constructed a CNN model that can recognize BSs with moderate accuracy, thus providing a putative non-invasive tool for conveniently determining gut health and demonstrating the potential of automated BS research. [ABSTRACT FROM AUTHOR]

Pulmonary air leak is the most common complication of lung surgery, with air leaks that persist longer than 5 days representing a major source of post-surgery morbidity. Clinical management of air leaks is challenging due to limited methods to precisely locate and assess leaks. Here, we present a sound-guided methodology that enables rapid quantitative assessment and precise localization of air leaks by analyzing the distinct sounds generated as the air escapes through defective lung tissue. Air leaks often present after lung surgery due to loss of tissue integrity at or near a staple line. Accordingly, we investigated air leak sounds from a focal pleural defect in a rat model and from a staple line failure in a clinically relevant swine model to demonstrate the high sensitivity and translational potential of this approach. In rat and swine models of free-flowing air leak under positive pressure ventilation with intrapleural microphone 1 cm from the lung surface, we identified that: (a) pulmonary air leaks generate sounds that contain distinct harmonic series, (b) acoustic characteristics of air leak sounds can be used to classify leak severity, and (c) precise location of the air leak can be determined with high resolution (within 1 cm) by mapping the sound loudness level across the lung surface. Our findings suggest that sound-guided assessment and localization of pulmonary air leaks could serve as a diagnostic tool to inform air leak detection and treatment strategies during video-assisted thoracoscopic surgery (VATS) or thoracotomy procedures. [ABSTRACT FROM AUTHOR]

Copyright of Fluminensia is the property of Fluminensia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background/aim Phonetics is an important function of the oral cavity that can be affected by partial or total loss of teeth. This study aimed to examine the influence of the partial denture’s construction technique (conventional and CAD/CAM) on speech quality through acoustic analysis of sound production of letters S, Sh, T, and D using sound analysis software. Patients and methods The study was conducted on 20 patients aged from 25 to 50 years having maxillary anterior teeth loss (Kennedy class IV classification). The patients were randomly categorized into two groups: group I received conventional removable partial dentures (RPDs), while group II received CAD/CAM RPDs. Acoustic speech analysis of the selected letters (S, Sh, T, and D) of the delivered RPDs was assessed and analyzed using ANSYS sound software and SIL speech Analyzer, 1 and 3 months after denture insertion. Statistical analysis was carried out using paired t test and independent t test. Results Group II showed significant difference (P0.05) in the acoustic pronunciation of the S, Sh, and T sound compared to group I. Within group I and group II separately, they revealed significant difference for all the measured parameters except for VOT/(D) which was insignificantly different. Conclusion RPDs constructed by CAD/CAM technology have a more precise fit of the chrome–cobalt framework with a high quality of sound production even at the time of denture insertion.

Abstract Pulmonary air leak is the most common complication of lung surgery, with air leaks that persist longer than 5 days representing a major source of post‐surgery morbidity. Clinical management of air leaks is challenging due to limited methods to precisely locate and assess leaks. Here, we present a sound‐guided methodology that enables rapid quantitative assessment and precise localization of air leaks by analyzing the distinct sounds generated as the air escapes through defective lung tissue. Air leaks often present after lung surgery due to loss of tissue integrity at or near a staple line. Accordingly, we investigated air leak sounds from a focal pleural defect in a rat model and from a staple line failure in a clinically relevant swine model to demonstrate the high sensitivity and translational potential of this approach. In rat and swine models of free‐flowing air leak under positive pressure ventilation with intrapleural microphone 1 cm from the lung surface, we identified that: (a) pulmonary air leaks generate sounds that contain distinct harmonic series, (b) acoustic characteristics of air leak sounds can be used to classify leak severity, and (c) precise location of the air leak can be determined with high resolution (within 1 cm) by mapping the sound loudness level across the lung surface. Our findings suggest that sound‐guided assessment and localization of pulmonary air leaks could serve as a diagnostic tool to inform air leak detection and treatment strategies during video‐assisted thoracoscopic surgery (VATS) or thoracotomy procedures.

Der erste Satz (Prélude à la nuit) der Rapsodie Espagnole (1907–08) von Maurice Ravel wird hier in Bezug auf die integrative Funktion der Klangfarbe näher erörtert. Die folgende These bildet den Ausgangspunkt: In der Musik Ravels ist die Klangfarbe ein konstitutiver Faktor, der die Form wesentlich mitbestimmt. Klangfarbe und Form bilden ein multidimensionales Ganzes, das sich aus den unterschiedlichsten Perspektiven (Melodik, Harmonik, Energieverlauf, Klanghelligkeit, psychologische Expektanz u. a.) betrachten lässt. Ergebnis der Analyse ist eine Polyvalenz der Form – eine Spannung zwischen einer kreisförmigen Bewegung und einer stetigen Weitung und Öffnung, die der Geschlossenheit das Prinzip der Veränderung entgegensetzt. The first movement (Prélude à la nuit) of Maurice Ravel’s Rapsodie Espagnole (1907–08) is discussed in detail in regard to the integrative function of timbre. The following argument is applied as a point of departure: In Ravel’s music, timbre is a constitutive factor which co-determines musical form. Timbre and form are part of a multidimensional whole that can be viewed from a wide variety of perspectives (melody, harmony, energy flow, timbral brightness, psychological expectancy etc.). The result of the analysis is a polyvalence of form in this piece – a tension between a closed circular movement and a tendency of steady expansion.

In precision livestock, there has been a growing demand for innovative tools that collect and analyze information about individual animals. For this purpose, various variables of precision livestock such as monitoring the general condition of animals, activity and health status, food intake, or estrous activity are measured by using information technology. In recent years, the requirement for sound analysis to be used in these systems has increased. Because collecting sound signals do not require animal intervention. Dairy cattle make different sounds in cases of illness, pregnancy, feeding, etc., and by using sound signals, the diagnosis and status determination of the animal can be made. The aim of this study is to record the vocalization data of a dairy cattle in a semi-open barn and to investigate its differences from other barn sounds. It has been revealed that the frequency ranges of cattle, environment, bird, and machine sounds, which are analyzed by time domain, frequency domain, and spectrogram, are different and these differences can be used in a cattle identification system. [ABSTRACT FROM AUTHOR]

Why is it possible to distinguish between different textile fabrics by just touching them and moving your hand over them and listening to the sound? Particularly for high-quality woven fabrics, e.g. used for tailor-made suits, it is quite common that the dressmaker listens to the sound as their hand rubs the fabric. Can this approach be translated into a technical measurement? What could a sound analysis tell us about the fabric properties? As a first simple approach, we used a record player to rotate different cotton fabrics, and tested fine tips from diverse materials, such as plastic pipettes, pens, glass tips, etc. Our results show clear differences between the textile fabrics, which can be attributed to different yarn, knitted or woven structures. While the rotational mode of investigation impedes fully automated fast Fourier transform (FFT) evaluations, our first results suggest de¬veloping this promising method further.

Precision livestock farming (PLF) research is rapidly increasing and has improved farmers’ quality of life, animal welfare, and production efficiency. PLF research in dairy calves is still relatively recent but has grown in the last few years. Automatic milk feeding systems (AMFS) and 3D accelerometers have been the most extensively used technologies in dairy calves. However, other technologies have been emerging in dairy calves’ research, such as infrared thermography (IRT), 3D cameras, ruminal bolus, and sound analysis systems, which have not been properly validated and reviewed in the scientific literature. Thus, with this review, we aimed to analyse the state-of-the-art of technological applications in calves, focusing on dairy calves. Most of the research is focused on technology to detect and predict calves’ health problems and monitor pain indicators. Feeding and lying behaviours have sometimes been associated with health and welfare levels. However, a consensus opinion is still unclear since other factors, such as milk allowance, can affect these behaviours differently. Research that employed a multi-technology approach showed better results than research focusing on only a single technique. Integrating and automating different technologies with machine learning algorithms can offer more scientific knowledge and potentially help the farmers improve calves’ health, performance, and welfare, if commercial applications are available, which, from the authors’ knowledge, are not at the moment.

Bowel sound (BS) is receiving more attention as an indicator of gut health since it can be acquired non-invasively. Current gut health diagnostic tests require special devices that are limited to hospital settings. This study aimed to develop a prototype smartphone application that can record BS using built-in microphones and automatically analyze the sounds. Using smartphones, we collected BSs from 100 participants (age 37.6 ± 9.7). During screening and annotation, we obtained 5929 BS segments. Based on the annotated recordings, we developed and compared two BS recognition models: CNN and LSTM. Our CNN model could detect BSs with an accuracy of 88.9% andan F measure of 72.3% using cross evaluation, thus displaying better performance than the LSTM model (82.4% accuracy and 65.8% F measure using cross validation). Furthermore, the BS to sound interval, which indicates a bowel motility, predicted by the CNN model correlated to over 98% with manual labels. Using built-in smartphone microphones, we constructed a CNN model that can recognize BSs with moderate accuracy, thus providing a putative non-invasive tool for conveniently determining gut health and demonstrating the potential of automated BS research.