Sound levels and risk perceptions of music students during classes

It is well recognized that professional musicians are at risk of hearing damage due to the exposure to high sound pressure levels during music playing. However, it is important to recognize that the musicians’ exposure may start early in the course of their training as students in the classroom and at home. Studies regarding sound exposure of music students and their hearing disorders are scarce and do not take into account important influencing variables. Therefore, this study aimed to describe sound level exposures of music students at different music styles, classes, and according to the instrument played. Further, this investigation attempted to analyze the perceptions of students in relation to exposure to loud music and consequent health risks, as well as to characterize preventive behaviors. The results showed that music students are exposed to high sound levels in the course of their academic activity. This exposure is potentiated by practice outside the school and other external activities. Differences were found between music style, instruments, and classes. Tinnitus, hyperacusis, diplacusis, and sound distortion were reported by the students. However, students were not entirely aware of the health risks related to exposure to high sound pressure levels. These findings reflect the importance of starting intervention in relation to noise risk reduction at an early stage, when musicians are commencing their activity as students.

The effect of music marketing: A consumer perspective

The purpose of this thesis is to get a consumer perspective regarding event marketing in music festivals. Event marketing is a tool used by marketers that evolved out of philanthropy and commercial sponsorship. Brands are more and more using music or other entertainment moments to create a strong relationship with their clients, and the target group at these events, the millennial generation. Brands use sponsoring and therefore event marketing for several reasons as: increase brand awareness; create brand image; re-position the brand/product in the minds of consumers; increase profit over a short period; and, achieve larger market share. Nonetheless, we wonder how is this tool seen by consumers? To understand this, a preliminary research with nine interviews was conducted to obtain basic ideas about event marketing. Afterwards the main research was developed, also using interviews, to get deeper insights. With this thesis, it is possible to conclude that some brands are able to create brand awareness on attendees through brand sponsorship. Moreover, entertainment activities in festivals are well seen by consumers, they like it and are able to describe it well, even though it is more about the activity itself than the brand promoting it. Furthermore, it was possible to understand that experiential marketing in a festival might have a positive effect on consumers as it might create a link between the event and the brand. Finally, we recommend some actions, for brands to develop in future music festivals.

Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications

Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.

Automatic measurement of key ski jumping phases and temporal events with a wearable system.

We propose a new method, based on inertial sensors, to automatically measure at high frequency the durations of the main phases of ski jumping (i.e. take-off release, take-off, and early flight). The kinematics of the ski jumping movement were recorded by four inertial sensors, attached to the thigh and shank of junior athletes, for 40 jumps performed during indoor conditions and 36 jumps in field conditions. An algorithm was designed to detect temporal events from the recorded signals and to estimate the duration of each phase. These durations were evaluated against a reference camera-based motion capture system and by trainers conducting video observations. The precision for the take-off release and take-off durations (indoor < 39 ms, outdoor = 27 ms) can be considered technically valid for performance assessment. The errors for early flight duration (indoor = 22 ms, outdoor = 119 ms) were comparable to the trainers' variability and should be interpreted with caution. No significant changes in the error were noted between indoor and outdoor conditions, and individual jumping technique did not influence the error of take-off release and take-off. Therefore, the proposed system can provide valuable information for performance evaluation of ski jumpers during training sessions.

Educators' attitudes to philosophies of music education

This study used Q methodology to measure the extent to which individuals with five educational roles (student teacher, elementary music teacher, principal, high school music teacher, and music consultant) held five proposed philosophies of music education (hedonic, utilitarian, aesthetic cognitivism, aesthetic formalist, and praxial). Twenty-seven sUbjects participated in the Q study. These subjects were a convenience sample based on their educational role, accessibility, and willingness to participate. Participants completed a background sheet which indicated their background in music, and their responsibility for teaching music. The sUbjects in this Q study rank-ordered a set of 60 Q sort items (each item representing a proposed philosophical position) twice: Sort P to reflect current practice, and Sort I to reflect the ideal situation. The results of the sorting procedures were recorded by the participant on the response page which organized the rankings according to an approximated normal distribution as required by Q methodology. The analysis of the data suggested that the comparison across philosophical positions was significant and that the results of the interaction between philosophical position and educational role were significant, although educational role alone was not significant. Post-hoc analysis of the data was used to determine the significant differences between the levels of the, independent variables used in the model: philosophical position, educational role, and music background. A model of the association of the five philosophical positions was presented and discussed in relation to the Q study results. Further research could refine the Q sort items to better reflect each philosophical position.

The interpretation of music videos : an interactive relationship between medium and viewer

There is a great deal of evidence to support the examination of an interactive relationship between the medium and the viewer in the interpretation of mainstream media. The exact nature of this relationship, however, is not well understood. The current study was carried out to assess the variables that may help explain why certain people interpret media, such as music videos, differently than others. Jensen's concept of reception analysis describes the relationship between the medium and the audience, and thus remains a strong focus within this study. Differences in the interpretation of music videos were investigated as a function of Absorption, gender role, screen size, age and viewing experience. Multiple regression analyses uncovered independent predictions of sexuality and violence scores by absorption and experience, as well as an interaction between absorption and screen size in the sexuality rating of the music videos.

Bradley Institute of Music fonds

1.23 m. textual records, 1 col. post card, 1 b&w post card, 116 col. photographs, 59 b&w photographs, 6 negatives, 1 metal logo, 2 photo cuts, 7 woodcuts, 1 VHS tape, 1 stamp/press, 1 guest book, 5 account books

Automatic evolution of conceptual building architectures

This thesis describes research in which genetic programming is used to automatically evolve shape grammars that construct three dimensional models of possible external building architectures. A completely automated fitness function is used, which evaluates the three dimensional building models according to different geometric properties such as surface normals, height, building footprint, and more. In order to evaluate the buildings on the different criteria, a multi-objective fitness function is used. The results obtained from the automated system were successful in satisfying the multiple objective criteria as well as creating interesting and unique designs that a human-aided system might not discover. In this study of evolutionary design, the architectures created are not meant to be fully functional and structurally sound blueprints for constructing a building, but are meant to be inspirational ideas for possible architectural designs. The evolved models are applicable for today's architectural industries as well as in the video game and movie industries. Many new avenues for future work have also been discovered and highlighted.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Automatic Preparation of ETD Material from the Internet Archive for the DSpace Repository Platform

A big challenge associated with getting an institutional repository off the ground is getting content into it. This article will look at how to use digitization services at the Internet Archive alongside software utilities that the author developed to automate the harvesting of scanned dissertations and associated Dublin Core XML files to create an ETD Portal using the DSpace platform. The end result is a metadata-rich, full-text collection of theses that can be constructed for little out of pocket cost.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Narrative in the music of Jay Chou : a case study of music, globalization and semantics

La version intégrale de ce mémoire est disponible uniquement pour consultation individuelle à la Bibliothèque de musique de l’Université de Montréal (www.bib.umontreal.ca/MU).

Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals

On étudie l’application des algorithmes de décomposition matricielles tel que la Factorisation Matricielle Non-négative (FMN), aux représentations fréquentielles de signaux audio musicaux. Ces algorithmes, dirigés par une fonction d’erreur de reconstruction, apprennent un ensemble de fonctions de base et un ensemble de coef- ficients correspondants qui approximent le signal d’entrée. On compare l’utilisation de trois fonctions d’erreur de reconstruction quand la FMN est appliquée à des gammes monophoniques et harmonisées: moindre carré, divergence Kullback-Leibler, et une mesure de divergence dépendente de la phase, introduite récemment. Des nouvelles méthodes pour interpréter les décompositions résultantes sont présentées et sont comparées aux méthodes utilisées précédemment qui nécessitent des connaissances du domaine acoustique. Finalement, on analyse la capacité de généralisation des fonctions de bases apprises par rapport à trois paramètres musicaux: l’amplitude, la durée et le type d’instrument. Pour ce faire, on introduit deux algorithmes d’étiquetage des fonctions de bases qui performent mieux que l’approche précédente dans la majorité de nos tests, la tâche d’instrument avec audio monophonique étant la seule exception importante.

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques