Sofia Gubaidulina's violin concerto Offertorium : theology and music in dialogue

L’objectif de ce mémoire est de comprendre comment une certaine vision du monde, basée sur des croyances théologiques, a contribué à la composition du concerto pour violon Offertorium de Sofia Gubaïdulina. C’est par le biais de cette œuvre qu’est explorée l’idée du dialogue musicothéologique, en proposant des façons par lesquelles la pièce musicale en question peut servir de porteuse ou d’interprète d’une pensée théologique. Afin d’appuyer cette idée, la démarche intertextuelle employée par Heidi Epstein est utilisée. Cette méthode permet de faciliter non seulement le travail interdisciplinaire, mais aussi la lecture théologique de l’œuvre musicale. Le premier chapitre explore les sources, les questions et la problématique qui entoure le dialogue musicothéologique. La conclusion tirée est que l’étude d’Offertorium nécessite une approche équilibrée. Nous entendons par cela, une approche qui prend en ligne de compte la réflexion théologique autant que la recherche musicologique tout en respectant les contributions théologiques que l’œuvre musicale peut apporter en soi. Dans le deuxième chapitre, une analyse thématique d’Offertorium a été tentée ainsi qu’une étude du discours théologique et spirituel de la compositrice. Il a été conclu que l’arrière-plan russe orthodoxe de Gubaidulina a beaucoup influencé sa vision du monde et son approche artistique. Le concerto est porteur d’idées et de symboles liturgiques ou théologiques de l’Orthodoxie dans sa structure et dans sa construction thématique. Le troisième chapitre explore les parallèles entre la pensée de Gubaidulina et les écritures de plusieurs théologiens russes orthodoxes du 20e siècle. La conclusion de ce chapitre démontre que, même s’il est improbable que la compositrice connaisse bien ces auteurs, sa compréhension théologique et spirituelle sort du climat religieux de l’Église Orthodoxe. Cette idée explique les complémentarités et les similarités entre son discours, son œuvre et les propos des théologiens discutés. Le quatrième chapitre évalue la validité d’Offertorium comme moyen d’expression théologique ainsi que de générateur de réflexion théologique. La conclusion de la recherche est qu’Offertorium peut bel et bien être un espace théologique. Ce qui veut dire que des idées théologiques peuvent être communiquées par le biais de l’expérience sonore que ce soit par la mélodie ou l’ambiance générale. Également, cela implique que la musique devient un partenaire égal, quoique différent des méthodes de réflexion traditionnelles au sein de la conversation théologique.

Apprentissage de représentations musicales à l'aide d'architectures profondes et multiéchelles

L'apprentissage machine (AM) est un outil important dans le domaine de la recherche d'information musicale (Music Information Retrieval ou MIR). De nombreuses tâches de MIR peuvent être résolues en entraînant un classifieur sur un ensemble de caractéristiques. Pour les tâches de MIR se basant sur l'audio musical, il est possible d'extraire de l'audio les caractéristiques pertinentes à l'aide de méthodes traitement de signal. Toutefois, certains aspects musicaux sont difficiles à extraire à l'aide de simples heuristiques. Afin d'obtenir des caractéristiques plus riches, il est possible d'utiliser l'AM pour apprendre une représentation musicale à partir de l'audio. Ces caractéristiques apprises permettent souvent d'améliorer la performance sur une tâche de MIR donnée. Afin d'apprendre des représentations musicales intéressantes, il est important de considérer les aspects particuliers à l'audio musical dans la conception des modèles d'apprentissage. Vu la structure temporelle et spectrale de l'audio musical, les représentations profondes et multiéchelles sont particulièrement bien conçues pour représenter la musique. Cette thèse porte sur l'apprentissage de représentations de l'audio musical. Des modèles profonds et multiéchelles améliorant l'état de l'art pour des tâches telles que la reconnaissance d'instrument, la reconnaissance de genre et l'étiquetage automatique y sont présentés.

Who influence the music tastes of adolescents? A study on interpersonal influence in social networks

Research on music information behavior demonstrates that people rely primarily on others to discover new music. This paper reports on a qualitative study aiming at exploring more in-depth how music information circulates within the social networks of late adolescents and the role the different people involved in the process play. In-depth interviews were conducted with 19 adolescents (15-17 years old). The analysis revealed that music opinion leaders showed eagerness to share music information, tended to seek music information on an ongoing basis, and were perceived as being more knowledgeable than others in music. It was found that the ties that connected participants to opinion leaders were predominantly strong ties, which suggests that trustworthiness is an important component of credibility. These findings could potentially help identify new avenues for the improvement of music recommender systems.

The Wolf and the Whale: aesthetic relationships between electroacoustic music and poetry inspired by the Canadian landscape

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

Composing idiomatically for specific performers : collaboration in the creation of electroacoustic music

Cette thèse examine l'impact de la collaboration avec des instrumentistes particuliers sur la composition de quatre œuvres électroacoustiques. Assumant un rôle plus important que celui de consultant ou conseiller, les interprètes ont influencé les décisions de l'auteur / compositeur dans le cadre de multiples ateliers et d'enregistrements de ceux-ci. Cette thèse examine ainsi comment les outils médiatiques de la musique électroacoustique affectent et enrichissent les relations personnelles : ces outils favorisent la transcription et la traduction, qui à la fois soulignent et transforment la spécificité du son. Le dialogue de la collaboration permet par la suite non seulement une réconciliation plus facile entre les éléments médiatisés et directs dans une oeuvre, mais aussi l'ouverture de son potentiel d'interprétation. En se servant d'une méthodologie qui fait appel à une pratique d'auto-réflexion et récursivité, cette thèse explore des sujets tels que : l'analyse du style personnel dans un cadre linguistique; l'importance du contact physique dans la collaboration et sa traduction incomplète sur support; et les défis de la préservation de la musique électroacoustique pour média ou interprète particulier. Des exemples de la création collaborative de quatre œuvres, racontés de manière personnelle, sont tressés parmi le récit plus théorique de cette thèse, imitant le va-et-vient de la recherche-création.

Real Time Raga Detection and Analysis Using Computer

Department of Computer Applications, Cochin University of Science and Technology

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Development of a Fully Automated Image Analysis Method for High Density cDNA and array CGH Microarray Based Genomic Studies

In this thesis, different techniques for image analysis of high density microarrays have been investigated. Most of the existing image analysis techniques require prior knowledge of image specific parameters and direct user intervention for microarray image quantification. The objective of this research work was to develop of a fully automated image analysis method capable of accurately quantifying the intensity information from high density microarrays images. The method should be robust against noise and contaminations that commonly occur in different stages of microarray development.

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.

The Effect of SIFT Features as Content Descriptors in the Context of Automatic Writer Identification in Malayalam Language

The span of writer identification extends to broad domes like digital rights administration, forensic expert decisionmaking systems, and document analysis systems and so on. As the success rate of a writer identification scheme is highly dependent on the features extracted from the documents, the phase of feature extraction and therefore selection is highly significant for writer identification schemes. In this paper, the writer identification in Malayalam language is sought for by utilizing feature extraction technique such as Scale Invariant Features Transform (SIFT).The schemes are tested on a test bed of 280 writers and performance evaluated

Automatic Segmentation Framework for Primary Tumors From Brain MRIs Using Morphological Filtering Techniques

This paper describes a novel framework for automatic segmentation of primary tumors and its boundary from brain MRIs using morphological filtering techniques. This method uses T2 weighted and T1 FLAIR images. This approach is very simple, more accurate and less time consuming than existing methods. This method is tested by fifty patients of different tumor types, shapes, image intensities, sizes and produced better results. The results were validated with ground truth images by the radiologist. Segmentation of the tumor and boundary detection is important because it can be used for surgical planning, treatment planning, textural analysis, 3-Dimensional modeling and volumetric analysis

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.

Automatic Optic Disc Boundary Extraction from Color Fundus Images

Efficient optic disc segmentation is an important task in automated retinal screening. For the same reason optic disc detection is fundamental for medical references and is important for the retinal image analysis application. The most difficult problem of optic disc extraction is to locate the region of interest. Moreover it is a time consuming task. This paper tries to overcome this barrier by presenting an automated method for optic disc boundary extraction using Fuzzy C Means combined with thresholding. The discs determined by the new method agree relatively well with those determined by the experts. The present method has been validated on a data set of 110 colour fundus images from DRION database, and has obtained promising results. The performance of the system is evaluated using the difference in horizontal and vertical diameters of the obtained disc boundary and that of the ground truth obtained from two expert ophthalmologists. For the 25 test images selected from the 110 colour fundus images, the Pearson correlation of the ground truth diameters with the detected diameters by the new method are 0.946 and 0.958 and, 0.94 and 0.974 respectively. From the scatter plot, it is shown that the ground truth and detected diameters have a high positive correlation. This computerized analysis of optic disc is very useful for the diagnosis of retinal diseases

Analysis and Implementation of Robust Grasping Behaviors

This thesis addresses the problem of developing automatic grasping capabilities for robotic hands. Using a 2-jointed and a 4-jointed nmodel of the hand, we establish the geometric conditions necessary for achieving form closure grasps of cylindrical objects. We then define and show how to construct the grasping pre-image for quasi-static (friction dominated) and zero-G (inertia dominated) motions for sensorless and sensor-driven grasps with and without arm motions. While the approach does not rely on detailed modeling, it is computationally inexpensive, reliable, and easy to implement. Example behaviors were successfully implemented on the Salisbury hand and on a planar 2-fingered, 4 degree-of-freedom hand.

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