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- All Subjects: Songs (High voice) with piano
ContributorsWasbotten, Leia (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-30
Description
Libby Larsen is one of the most performed and acclaimed composers today. She is a spirited, compelling, and sensitive composer whose music enhances the poetry of America's most prominent authors. Notable among her works are song cycles for soprano based on the poetry of female writers, among them novelist and poet Willa Cather (1873-1947). Larsen has produced two song cycles on works from Cather's substantial output of fiction: one based on Cather's short story, "Eric Hermannson's Soul," titled Margaret Songs: Three Songs from Willa Cather (1996); and later, My Antonia (2000), based on Cather's novel of the same title. In Margaret Songs, Cather's poetry and short stories--specifically the character of Margaret Elliot--combine with Larsen's unique compositional style to create a surprising collaboration. This study explores how Larsen in these songs delves into the emotional and psychological depths of Margaret's character, not fully formed by Cather. It is only through Larsen's music and Cather's poetry that Margaret's journey through self-discovery and love become fully realized. This song cycle is a glimpse through the eyes of two prominent female artists on the societal pressures placed upon Margaret's character, many of which still resonate with women in today's culture. This study examines the work Margaret Songs by discussing Willa Cather, her musical influences, and the conditions surrounding the writing of "Eric Hermannson's Soul." It looks also into Cather's influence on Libby Larsen and the commission leading to Margaret Songs. Finally, a description of the musical, dramatic, and textual content of the songs completes this interpretation of the interactions of Willa Cather, Libby Larsen, and the character of Margaret Elliot.
ContributorsMcLain, Christi Marie (Author) / FitzPatrick, Carole (Thesis advisor) / Dreyfoos, Dale (Committee member) / Holbrook, Amy (Committee member) / Ryan, Russell (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
ContributorsYi, Joyce (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-22
ContributorsCummiskey, Hannah (Performer) / Kim, Olga (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-23
ContributorsEvans, Emily (Performer) / Sherrill, Amanda (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-02
Description
Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems, including line formation, compression, and coating. In this work we study foundational computational tasks that exceed the capabilities of the individual constant memory particles described by the model. These tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. We present an implementation of a counter using a line of particles, which makes it possible for the line of particles to count to and store values much larger than their individual capacities. We then present an algorithm that takes a matrix and a vector as input and then sets up and uses a rectangular block of particles to compute the matrix-vector multiplication. This setup also utilizes the counter implementation to store the resulting vector from the matrix-vector multiplication. Operations such as counting and matrix multiplication can leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Such computational tools also give the systems more power to make complex decisions when adapting to new situations or to analyze the data they collect, reducing reliance on a central controller for setup and output processing. Finally, we demonstrate an application of similar types of computations with self-organizing systems to image processing, with an implementation of an image edge detection algorithm.
ContributorsPorter, Alexandra Marie (Author) / Richa, Andrea (Thesis director) / Xue, Guoliang (Committee member) / School of Music (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
ContributorsMartorana, Gabrielle (Performer) / Olarte, Aida (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-20
Description
Imagine that we have a piece of matter that can change its physical properties like its shape, density, conductivity, or color in a programmable fashion based on either user input or autonomous sensing. This is the vision behind what is commonly known as programmable matter. Envisioning systems of nano-sensors devices, programmable matter consists of systems of simple computational elements, called particles, that can establish and release bonds, compute, and can actively move in a self-organized way. In this dissertation the feasibility of solving fundamental problems relevant for programmable matter is investigated. As a model for such self-organizing particle systems (SOPS), the geometric amoebot model is introduced. In this model, particles only have local information and have modest computational power. They achieve locomotion by expanding and contracting, which resembles the behavior of amoeba. Under this model, efficient local-control algorithms for the leader election problem in SOPS are presented. As a central problem for programmable matter, shape formation problems are then studied. The limitations of solving the leader election problem and the shape formation problem on a more general version of the amoebot model are also discussed. The \smart paint" problem is also studied which aims at having the particles self-organize in order to uniformly coat the surface of an object of arbitrary shape and size, forming multiple coating layers if necessary. A Universal Coating algorithm is presented and shown to be asymptotically worst-case optimal both in terms of time with high probability and work. In particular, the algorithm always terminates within a linear number of rounds with high probability. A linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information is presented, which implies that the proposed algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of the proposed algorithm may be better than linear in practice. Developed algorithms utilize only local control, require only constant-size memory particles, and are asymptotically optimal in terms of the total number of particle movements needed to reach the desired shape configuration.
ContributorsDerakhshandeh, Zahra (Author) / Richa, Andrea (Thesis advisor) / Sen, Arunabha (Thesis advisor) / Xue, Guoliang (Committee member) / Scheideler, Christian (Committee member) / Arizona State University (Publisher)
Created2017