The currently low requirements on sound emissions for industrial trucks are expected to be raised in the near future. The company Kalmar Industries AB, which develop, produce and market industrial trucks, want to improve their truck silencers as a precaution to the future harder restrictions and also to improve their competitiveness. The sound emission produced by a vehicle depends on type and range of application it is designed for but the dominant part of the sound is usually produced by the engine and silencer. A new measuring method is developed for studying sound emanating through the silencer system. The analysis of the measurement data establishes that the silencers are not working well. The simulations made with SIDLAB, which is a computer programme for calculating the propagation of sound in ducts, confirms that the silencers are inefficient and that they are simply too small. A simulation which implements a parallel resonator in the main silencer shows that it is possible to make great improvements in reducing the noise from the truck as well as meeting requirements on space. Mathematical modelling based on linearity and one-dimensional interaction between the silencer elementsis advantageous and gives very good results when understanding, analysing and simulating the silencer. The simulation tool SIDLAB works well and saves a lot of time by its fast modelling and easy interface.

<p>The currently low requirements on sound emissions for industrial trucks are expected to be raised in the near future. The company Kalmar Industries AB, which develop, produce and market industrial trucks, want to improve their truck silencers as a precaution to the future harder restrictions and also to improve their competitiveness. The sound emission produced by a vehicle depends on type and range of application it is designed for but the dominant part of the sound is usually produced by the engine and silencer.</p><p>A new measuring method is developed for studying sound emanating through the silencer system. The analysis of the measurement data establishes that the silencers are not working well. The simulations made with SIDLAB, which is a computer programme for calculating the propagation of sound in ducts, confirms that the silencers are inefficient and that they are simply too small.</p><p>A simulation which implements a parallel resonator in the main silencer shows that it is possible to make great improvements in reducing the noise from the truck as well as meeting requirements on space.</p><p>Mathematical modelling based on linearity and one-dimensional interaction between the silencer elementsis advantageous and gives very good results when understanding, analysing and simulating the silencer. The simulation tool SIDLAB works well and saves a lot of time by its fast modelling and easy interface.</p>

Introduction: Obstructive sleep apnea (OSA) is a common breathing disorder with numerous health consequences, including greater risk of complications perioperatively. Undiagnosed OSA is known to place surgical patients at a higher risk of serious adverse events, including stroke and death. Polysomnography (PSG) assessment is the current gold standard test for diagnosing OSA. However, due to the significant time commitment and cost associated with PSG, a substantial number of OSA patients go undiagnosed before the perioperative period. Although the STOP-Bang questionnaire screening tool is currently used to help detect OSA patients, the low specificity to screen people without the disease is considered a major limitation. There is a clear need to develop a quick and effective prediction rule with higher overall accuracy to help streamline OSA diagnosis. Tracheal breathing sound analysis in awake patients at the bedside has shown potential to screen OSA patients with higher specificity compared to the STOP-Bang questionnaire. To date, no screening tools exist to detect OSA patients that combine the results of breathing sound analysis and STOP-Bang. Objectives: The present study aimed to develop a prediction rule, using both breathing sound analysis and variables in the STOP-Bang questionnaire, to better streamline the diagnosis of OSA. Methods: This prospective cohort study recruited patients referred for PSG at the Ottawa Hospital Sleep Centre from November 2016 to May 2017. The study conduct was approved by the Ottawa Health Science Network Research Ethics Board (#20160494-01H). After obtaining informed consent, anthropomorphic, breathing sound recordings, and STOP-Bang questionnaire data was collected from over 400 consenting patients. All patients that met the eligibility criteria were included. The breathing sound analysis and STOP-Bang results were utilized to design a prediction rule using logistic regression. Sensitivity, specificity, and likelihood ratio were used to compare the diagnostic performance of the final model. Results: Of the 439 consenting study participants, 280 study participants data were eligible for inclusion in the logistic regression analysis. Physician sleep specialists diagnosed 114 participants (41%) with moderate-to-severe OSA and 166 participants (59%) with normal-to-mild OSA. At a predicted probability of moderate-to-severe OSA greater than or equal to 0.5, breathing sound analysis had a similar sensitivity of 75.9 (95%CI; 65.4, 82.0) and higher specificity of 74.5% (95%CI; 68.5, 82.0) when compared to STOP-Bang with a sensitivity and specificity of 68.4% (95%CI; 58.9, 76.6) and 63.2% (95%CI: 55.0, 70.1), respectively. The sensitivity and specificity for the Safe-OSA rule, obtained by combining breathing sound analysis and STOP-Bang variables, were determined to be 75.4% (95%CI; 65.4, 82.0) and 74.5% (95%CI; 68.5, 82.0), respectively. A sensitivity analysis using a likelihood ratio test showed that breathing sound analysis contributed significantly to the performance of the Safe-OSA rule. The Safe-OSA rule was determined to be reasonably discriminative and well calibrated. The five-fold cross-validation showed similar results for the final model in the derivation and testing subsamples, which provides support for the internal validity of the Safe-OSA rule in our study population. Conclusion: The present study lends further support for the future testing of tracheal breathing sound analysis as a potential method to screen for moderate-to-severe OSA to help streamline patient care in the perioperative setting. Trial registration: ClinicalTrials.gov identifier NCT02987283.

Honeybees are an essential part of our ecosystem as they take care of most of the pollination in the world. They also produce honey, which is the main reason beekeeping was introduced in the first place. As the production of honey is affected by the living conditions of the honeybees, the beekeepers aim to maintain the health of the honeybee societies. TietoEVRY, together with HSB Living Lab, introduced connected beehives in a project named BeeLab. The goal of BeeLab is to provide a service to monitor and gain knowledge about honeybees using the data collected with different sensors. Today they measure weight, temperature, air pressure, and humidity. It is known that honeybees produce different sounds when different events are occurring in the beehive. Therefore BeeLab wants to introduce sound monitoring to their service. This project aims to investigate the possibility of detecting deviations in beehives based on sound analysis and machine learning. This includes recording sound from beehives followed by preprocessing of sound data, feature extraction, and applying a machine learning algorithm on the sound data. An experiment is done using Mel-Frequency Cepstral Coefficients (MFCC) to extract sound features and applying the DBSCAN machine learning algorithm to investigate the possibilities of detecting deviations in the sound data. The experiment showed promising results as deviating sounds used in the experiment were grouped into different clusters.

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For the past few decades, researchers have increased our understanding of how sound functions within various audio–visual media formats. With a different focus in mind, this study aims to identify the roles and functions of sound in relation to the game form Audio Games, in order to explore the potential of sound when acting as an autonomous narrative form. Because this is still a relatively unexplored research field, the main purpose of this study is to help establish a theoretical ground and stimulate further research within the field of audio games. By adopting an interdisciplinary approach to the topic, this research relies on theoretical studies, examinations of audio games and contact with the audio game community. In order to reveal the roles of sound, the gathered data is analyzed according to both a contextual and a functional perspective. The research shows that a distinction between the terms ‘function’ and ‘role’ is important when analyzing sound in digital games. The analysis therefore results in the identification of two analytical levels that help define the functions and roles of an entity within a social context, named the Functional and the Interfunctional levels. In addition to successfully identifying three main roles of sound within audio games—each describing the relationship between sound and the entities game system, player and virtual environment—many other issues are also addressed. Consequently, and in accordance with its purpose, this study provides a broad foundation for further research of sound in both audio games and video games.

Στην παρούσα διπλωματική εργασία γίνεται μελέτη της ανάλυσης και σύνθεσης ήχου με τη βοήθεια ημιτονοειδών. Ειδικότερα, εξετάζονται οι παράμετροι της ανάλυσης και σύνθεσης και πως αυτες επηρεάζουν την τελική ανακατασκευή του σήματος. Στη συνέχεια γίνεται διερεύνηση της ανάλυσης και σύνθεσης μόνο στις χαμηλές συχνότητες. Με βάση ένα περιορισμένο εύρος ζώνης, γίνεται ανίχνευση των τονικών υψών. Αναπτύσσονται τρεις μέθοδοι κατηγοριοποίησης τους και στη συνέχεια γίνεται μία αξιολόγηση των μεθόδων αυτών μέσω των μέτρων NMR και PEAQ. / In this degree thesis sound analysis and synthesis using sinusoidals is studied. More specifically, parameters of analysis and synthesis are examined and how they affect the final reconstruction of a signal. Further research is conducted for analysis and synthesis at low sound frequencies. Based on a limited bandwidth, pitch detection is taking place on the input signal. Three methods of categorizing frequencies are developed and they are evaluated using the metrics of NMR (Noise to Mask Ratio) and PEAQ (Perceptual Evaluation of Audio Quality).

No description available.

Snoring is a sleep disruption that can lead to obstructive sleep apnea (OSA), which interrupts breathing by obstructing the airway. Injecting a protein crosslinker, such as genipin, into the soft palate could decrease the severity of snoring and OSA by stiffening the soft palate. Equine soft palates modeled human palates due to a high incidence of awake snoring and apnea. The pilot in vivo study treated six horses with two 100 mM injections of the buffered genipin reagent. The efficacy phase horses underwent respiratory audio recordings to document snoring changes using Matlab and ImageJ in the time and frequency domains. Histological analysis was completed on the safety phase palates post treatment. All horses were successfully treated with the genipin injections. At least one horse showed high frequency amplitude reductions, and all horses had low frequency amplitude reductions, correlating to a reduction in palatal displacement and snoring loudness. One efficacy horse appears to have been completely cured. The histological analysis presented tissue damage, mucosal tissue damage, and mild inflammation due to palate expansion and errant injections. Different injection volumes and techniques should be investigated next. Applying this treatment to human studies for snoring and OSA applications is the ultimate goal.

Tracheal respiratory sounds analysis has been investigated as a non-invasive method to estimate respiratory flow and upper airway obstruction. However, the flow-sound relationship is highly variable among subjects which makes it challenging to estimate flow in general applications. Therefore, a robust model for acoustical flow estimation in a large group of individuals did not exist before. On the other hand, a major application of acoustical flow estimation is to detect flow limitations in patients with obstructive sleep apnea (OSA) during sleep. However, previously the flow--sound relationship was only investigated during wakefulness among healthy individuals. Therefore, it was necessary to examine the flow-sound relationship during sleep in OSA patients. This thesis takes the above challenges and offers innovative solutions. First, a modified linear flow-sound model was proposed to estimate respiratory flow from tracheal sounds. To remove the individual based calibration process, the statistical correlation between the model parameters and anthropometric features of 93 healthy volunteers was investigated. The results show that gender, height and smoking are the most significant factors that affect the model parameters. Hence, a general acoustical flow estimation model was proposed for people with similar height and gender. Second, flow-sound relationship during sleep and wakefulness was studied among 13 OSA patients. The results show that during sleep and wakefulness, flow-sound relationship follows a power law, but with different parameters. Therefore, for acoustical flow estimation during sleep, the model parameters should be extracted from sleep data to have small errors. The results confirm reliability of the acoustical flow estimation for investigating flow variations during both sleep and wakefulness. Finally, a new method for sleep apnea detection and monitoring was developed, which only requires recording the tracheal sounds and the blood's oxygen saturation level (SaO2) data. It automatically classifies the sound segments into breath, snore and noise. A weighted average of features extracted from sound segments and SaO2 signal was used to detect apnea and hypopnea events. The performance of the proposed approach was evaluated on the data of 66 patients. The results show high correlation (0.96,p < 0.0001) between the outcomes of our system and those of the polysomnography. Also, sensitivity and specificity of the proposed method in differentiating simple snorers from OSA patients were found to be more than 91%. These results are superior or comparable with the existing commercialized sleep apnea portable monitors.