Matching Items (193)
Description
A new arrangement of the Concerto for Two Horns in E-flat Major, Hob. VIId/6, attributed by some to Franz Joseph Haydn, is presented here. The arrangement reduces the orchestral portion to ten wind instruments, specifically a double wind quintet, to facilitate performance of the work. A full score and a complete set of parts are included. In support of this new arrangement, a discussion of the early treatment of horns in pairs and the subsequent development of the double horn concerto in the eighteenth century provides historical context for the Concerto for Two Horns in E-flat major. A summary of the controversy concerning the identity of the composer of this concerto is followed by a description of the content and structure of each of its three movements. Some comments on the procedures of the arrangement complete the background information.
ContributorsYeh, Guan-Lin (Author) / Ericson, John (Thesis advisor) / Holbrook, Amy (Committee member) / Micklich, Albie (Committee member) / Pilafian, J. Samuel (Committee member) / Arizona State University (Publisher)
Created2011
Description
The purpose of this project was to commission, perform, and discuss a new work for an instrument pairing not often utilized, oboe and percussion. The composer, Alyssa Morris, was selected in June 2009. Her work, titled Forecast, was completed in October of 2009 and premiered in February of 2010, as part of a program showcasing music for oboe and percussion. Included in this document is a detailed biography of the composer, a description of the four movements of Forecast, performance notes for each movement, a diagram for stage set-up, the full score, the program from the premiere performance with biographies of all the performers involved, and both a live recording and MIDI sound file. The performance notes discuss issues that arose during preparation for the premiere and should help avoid potential pitfalls. TrevCo Music, publisher of the work, graciously allowed inclusion of the full score. This score is solely for use in this document; please visit the publisher's website for purchasing information. The commission and documentation of this composition are intended to add to the repertoire for oboe in an unusual instrument pairing and to encourage further exploration of such combinations.
ContributorsCreamer, Caryn (Author) / Schuring, Martin (Thesis advisor) / Hill, Gary (Committee member) / Holbrook, Amy (Committee member) / Micklich, Albie (Committee member) / Spring, Robert (Committee member) / Arizona State University (Publisher)
Created2011
Description
Although one finds much scholarship on nineteenth-century music in America, one finds relatively little about music in the post-Civil-War frontier west. Generalities concerning small frontier towns of regional importance remain to be discovered. This paper aims to contribute to scholarship by chronicling musical life in the early years of two such towns in northern Arizona territory: Prescott and Flagstaff. Prescott, adjacent to Fort Whipple, was founded in 1864 to serve as capital of the new territory. Primarily home to soldiers and miners, the town was subject to many challenges of frontier life. Flagstaff, ninety miles to the north-northwest, was founded about two decades later in 1883 during the building of the Atlantic & Pacific Railroad, which connected the town to Albuquerque, New Mexico in the east and southern California in the west. Although the particular resources of each town provided many different musical opportunities, extant newspaper articles from Prescott's Arizona Miner and Flagstaff's Arizona Champion describe communities in which musical concerts, dances and theatrical performances provided entertainment and socializing for its citizens. Furthermore, music was an important part of developing institutions such as the church, schools, and fraternal lodges, and the newspapers of both towns advertised musical instruments and sheet music. Both towns were home to amateur musicians, and both offered the occasional opportunity to learn to dance or play an instrument. Although territorial Arizona was sometimes harsh and resources were limited, music was valued in these communities and was a consistent presence in frontier life.
ContributorsJohnson, Amber V (Author) / Oldani, Robert W. (Thesis advisor) / Holbrook, Amy (Committee member) / Saucier, Catherine (Committee member) / Arizona State University (Publisher)
Created2011
Description
The theme for this work is the development of fast numerical algorithms for sparse optimization as well as their applications in medical imaging and source localization using sensor array processing. Due to the recently proposed theory of Compressive Sensing (CS), the $\ell_1$ minimization problem attracts more attention for its ability to exploit sparsity. Traditional interior point methods encounter difficulties in computation for solving the CS applications. In the first part of this work, a fast algorithm based on the augmented Lagrangian method for solving the large-scale TV-$\ell_1$ regularized inverse problem is proposed. Specifically, by taking advantage of the separable structure, the original problem can be approximated via the sum of a series of simple functions with closed form solutions. A preconditioner for solving the block Toeplitz with Toeplitz block (BTTB) linear system is proposed to accelerate the computation. An in-depth discussion on the rate of convergence and the optimal parameter selection criteria is given. Numerical experiments are used to test the performance and the robustness of the proposed algorithm to a wide range of parameter values. Applications of the algorithm in magnetic resonance (MR) imaging and a comparison with other existing methods are included. The second part of this work is the application of the TV-$\ell_1$ model in source localization using sensor arrays. The array output is reformulated into a sparse waveform via an over-complete basis and study the $\ell_p$-norm properties in detecting the sparsity. An algorithm is proposed for minimizing a non-convex problem. According to the results of numerical experiments, the proposed algorithm with the aid of the $\ell_p$-norm can resolve closely distributed sources with higher accuracy than other existing methods.
ContributorsShen, Wei (Author) / Mittlemann, Hans D (Thesis advisor) / Renaut, Rosemary A. (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Gelb, Anne (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2011
Description
Owen Middleton (b. 1941) enjoys an established and growing reputation as a composer of classical guitar music, but his works for piano are comparatively little known. The close investigation offered here of Middleton's works for piano reveals the same impressive craftsmanship, compelling character, and innovative spirit found in his works for guitar. Indeed, the only significant thing Middleton's piano music currently lacks is the well-deserved attention of professional players and a wider audience. Middleton's piano music needs to be heard, not just discussed, so one of this document's purposes is to provide a recorded sample of his piano works. While the overall repertoire for solo piano is vast, and new works become established in that repertoire with increasing difficulty, Middleton's piano works have a significant potential to find their way into the concert hall as well as the private teaching studio. His solo piano music is highly effective, well suited to the instrument, and, perhaps most importantly, fresh sounding and truly original. His pedagogical works are of equal value. Middleton's piano music offers something for everyone: there one finds daring virtuosity, effusions of passion, intellectual force, colorful imagery, poetry, humor, and even a degree of idiomatic innovation. This study aims to reveal key aspects of the composer's musical style, especially his style of piano writing, and to provide pianists with helpful analytical, technical, and interpretive insights. These descriptions of the music are supported with recorded examples, selected from the works for solo piano written between 1962 and 1993: Sonata for Piano, Childhood Scenes, Katie's Collection, and Toccata for Piano. The complete scores of the recorded works are included in the appendix. A chapter briefly describing the piano pieces since 1993 concludes the study and invites the reader to further investigations of this unique and important body of work.
ContributorsMoreau, Barton Andrew (Author) / Hamilton, Robert (Thesis advisor) / Holbrook, Amy (Committee member) / Campbell, Andrew (Committee member) / Spring, Robert (Committee member) / Gardner, Joshua (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011
Description
Peter N. Schubert in "Hidden Forms in Palestrina's `First Book of Four-Voice Motets'" (Journal of the American Musicological Society, 2007) defines significant blocks of vertical relationships in imitative and non-imitative duos in the thirty-six motets of Palestrina's Motectus festorum totius anni cum communi sanctorum, published in 1564. Schubert describes these blocks of vertical relationships that proceed from duos as modules and organizes them according to categories of construction and function. Palestrina's parody Mass, O Rex glóriæ, reveals the same duos and modules that Schubert discovers in Palestrina's motet of the same name. Palestrina transfers these duos and modules from the motet into the parody Mass, using them as building blocks for points of imitation. The duos, modules, and their motives appear in all but a few places, and are in some cases prominent throughout movements of the Mass, such as the Kyrie. Palestrina manipulates and elaborates these duos and modules according to the character and text of each movement. He borrows them consistently in their original order, which he changes only for reasons of textual meaning or verbal similarity. The module approach to recurring vertical combinations, although a recent application, is valuable for recognizing and treating systematically the duo relationships and their elaboration that are described by late-Renaissance theorists, especially Fray Tomas de Sancte Maria. The identification and analytical interpretation of duos and modules in Palestrina's motet O Rex glóriæ and the parody Mass based on it yields insights not only into his compositional decisions as he adapts material from the motet for its new setting, but also into the potential value of modules as the basis for an analytical approach to the sacred vocal polyphony of the sixteenth century.
ContributorsMenefee, Catherine Ann (Author) / Holbrook, Amy (Thesis advisor) / Saucier, Catherine (Committee member) / Carpenter, Ellon (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Discovering Puerto Rican art song: a research project on four art song works by Héctor Campos Parsi
Description
Puerto Rico has produced many important composers who have contributed to the musical culture of the nation during the last 200 years. However, a considerable amount of their music has proven to be difficult to access and may contain numerous errors. This research project intends to contribute to the accessibility of such music and to encourage similar studies of Puerto Rican music. This study focuses on the music of Héctor Campos Parsi (1922-1998), one of the most prominent composers of the 20th century in Puerto Rico. After an overview of the historical background of music on the island and the biography of the composer, four works from his art song repertoire are given for detailed examination. A product of this study is the first corrected edition of his cycles Canciones de Cielo y Agua, Tres Poemas de Corretjer, Los Paréntesis, and the song Majestad Negra. These compositions date from 1947 to 1959, and reflect both the European and nationalistic writing styles of the composer during this time. Data for these corrections have been obtained from the composer's manuscripts, published and unpublished editions, and published recordings. The corrected scores are ready for publication and a compact disc of this repertoire, performed by soprano Melliangee Pérez and the author, has been recorded to bring to life these revisions. Despite the best intentions of the author, the various copyright issues have yet to be resolved. It is hoped that this document will provide the foundation for a resolution and that these important works will be available for public performance and study in the near future.
ContributorsRodríguez Morales, Luis F., 1980- (Author) / Campbell, Andrew (Thesis advisor) / Buck, Elizabeth (Committee member) / Holbrook, Amy (Committee member) / Kopta, Anne (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2013