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.

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.

Detta kandidatarbete undersöker ljudbilder i film och vad som påverkar ljudbildens mättnad. Med stort fokus på Walter Murchs Dense Clarity, Clear Density (2005) bryter vi ner uppbyggnaden av en ljudbild för att få bättre förståelse kring detta. Med en egenframtagen analysmetod som fokuserar på filmers ljudbild analyserar vi scener ifrån flertalet filmer och tv-program, hittar mönster kring deras ljudläggning och hur det påverkar ljudbilden. Därefter bygger vi upp en lista med förhållningspunkter för olika typer av ljudbilder. Resultatet av undersökningen blir en förklaring av hur man uppnår olika former av ljudbilder i film och varför det blir så.

Gravel roads connect sparse populations and provide highways for agriculture and the transport of forest goods. Gravel roads are an economical choice where traffic volume is low. In Sweden, 21% of all public roads are state-owned gravel roads, covering over 20,200 km. In addition, there are some 74,000 km of gravel roads and 210,000 km of forest roads that are owned by the private sector. The Swedish Transport Administration (Trafikverket) rates the condition of gravel roads according to the severity of irregularities (e.g. corrugations and potholes), dust, loose gravel, and gravel cross-sections. This assessment is carried out during the summertime when roads are free of snow. One of the essential parameters for gravel road assessment is loose gravel. Loose gravel can cause a tire to slip, leading to a loss of driver control.  Assessment of gravel roads is carried out subjectively by taking images of road sections and adding some textual notes. A cost-effective, intelligent, and objective method for road assessment is lacking. Expensive methods, such as laser profiler trucks, are available and can offer road profiling with high accuracy. These methods are not applied to gravel roads, however, because of the need to maintain cost-efficiency.  In this thesis, we explored the idea that, in addition to machine vision, we could also use machine hearing to classify the condition of gravel roads in relation to loose gravel. Several suitable classical supervised learning and convolutional neural networks (CNN) were tested. When people drive on gravel roads, they can make sense of the road condition by listening to the gravel hitting the bottom of the car. The more we hear gravel hitting the bottom of the car, the more we can sense that there is a lot of loose gravel and, therefore, the road might be in a bad condition. Based on this idea, we hypothesized that machines could also undertake such a classification when trained with labeled sound data. Machines can identify gravel and non-gravel sounds. In this thesis, we used traditional machine learning algorithms, such as support vector machines (SVM), decision trees, and ensemble classification methods. We also explored CNN for classifying spectrograms of audio sounds and images in gravel roads. Both supervised learning and CNN were used, and results were compared for this study. In classical algorithms, when compared with other classifiers, ensemble bagged tree (EBT)-based classifiers performed best for classifying gravel and non-gravel sounds. EBT performance is also useful in reducing the misclassification of non-gravel sounds. The use of CNN also showed a 97.91% accuracy rate. Using CNN makes the classification process more intuitive because the network architecture takes responsibility for selecting the relevant training features. Furthermore, the classification results can be visualized on road maps, which can help road monitoring agencies assess road conditions and schedule maintenance activities for a particular road. / <p>Due to unforeseen circumstances the seminar was postponed from May 7 to 28, as duly stated in the new posting page.</p>

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La version intégrale de cette thèse est disponible uniquement pour consultation individuelle à la Bibliothèque de l'Université de Montréal (www.bib.umontreal.ca/MU). / Cette thèse portant sur le potentiel musical des analyses sonores consiste en une recherche créative et artistique portant sur des propriétés caractéristiques du comportement sonore mis en lumière via divers types d'analyses et susceptibles d'assister la composition musicale. Ce travail présente l'élaboration des outils d'analyses programmés et utilisés à cette fin, en plus d'un corpus de 6 oeuvres dont l'ordre de présentation correspond à leur chronologie d'écriture, et dont la conception et l'analyse musicales sont inégalement exposées (du bref aperçu au survol exhaustif). Au fur et à mesure de la recherche, le répertoire ci-développé intègre, par élargissement conceptuel, des considérations analytiques pour d'autres phénomènes vibratoires (comme la lumière ou l'harmonie des sphères) et, esthétiquement, toute cette démarche est fondée sur le concept aristotélicien de la mimesis, où l'idéal artistique consiste en la sublimation de la nature. / This thesis about the musical potential of sound analysis consists in an artistic and creative research on characteristic properties of sound behavior revealed by various types of analysis and likely to assist musical composing. This work presents the elaboration of analysis tools programmed and used on purpose, in addition of a corpus of six works which the order of presentation corresponds to their chronology of writing, and which both conception and musical analysis are unevenly exposed (from brief to exhaustive overviews). As the research occurs, the featured repertory integrates, by a conceptual enlargement, some analytic considerations for other vibratory phenomena (as light or the harmony of the spheres) and, aesthetically, all that approach is founded on the aristotelician concept of mimesis, in which the artistic ideal consists in the sublimation of nature.