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- Genre: Academic theses
- Genre: Doctoral Dissertation
- Creators: Yang, Yezhou
- Creators: Norton, Kay
- Member of: Theses and Dissertations
Description
New music is often created as a product of commissions resulting in a collaborative effort between the performer and the composer. This performer-composer relationship represents an important component of the role of the artist in expanding the repertoire of the instrument. Belgian composer, Norbert Goddaer (b. 1933), has written numerous works for clarinet that are the result of such collaborations. Mr. Goddaer's works for clarinet are well-crafted and audience-friendly, and are thus good programming choices for students and professionals alike. His clarinet works have been performed worldwide in artist recitals, conferences for organizations such as the International Clarinet Association, The Midwest Clinic, and the Texas Music Educators Association, and have been commercially recorded and released by some of the foremost contemporary clarinet artists. These works have a great education value given the fact that they are appropriate choices for such a wide range of clarinetists. In an effort to contribute to this body of performance history, the author has produced a recording of five of Goddaer's previously unrecorded works, accompanied by a performance guide to each work. This document provides detailed performance notes with corresponding musical examples, basic formal analyses, and musical suggestions for Las Mañas, Conversations, Ballad, Duets, and Restless by Norbert Goddaer. The author has included a full transcript of an interview with Norbert Goddaer, which includes a first-person discussion of each work, and additionally includes biographical information supported by concert programs and an annotated list of all of Goddaer's works for clarinet, and a discography of his works for clarinet.
ContributorsClasen, Kevin (Author) / Spring, Robert S (Thesis advisor) / Gardner, Joshua T (Committee member) / Norton, Kay (Committee member) / Hill, Gary (Committee member) / McAllister, Timothy (Committee member) / Arizona State University (Publisher)
Created2012
Description
Musical Impressionism has been most significantly reflected through the works of Claude Debussy (1862-1918) and Maurice Ravel (1875-1937). These two key figures exhibit the essence of this art and their piano music remains substantial, influential, and frequently assigned and played today. Nevertheless, from a pedagogical perspective, important factors required in achieving a successful performance of Debussy and Ravel's piano music--delicate tone production, independent voicing, complicated rhythm, sensitive pedaling, and a knowledgeable view of Impressionism--are musically and technically beyond the limit of early advanced students. This study provides a collection of short piano pieces by nine lesser-known European and American composers--Edward MacDowell (1861-1908), Charles Griffes (1884-1920), Marion Bauer (1887-1955), Cyril Scott (1879-1970), Arnold Bax (1883-1953), Selim Palmgren (1878-1951), Ottorino Respighi (1879-1936), Jacques Ibert (1890-1962) and Federico Mompou (1893-1987). They were influenced by impressionistic aesthetics or composed at one time in an impressionistic manner over a span of their lifetimes and their music provides a bridge to the more advanced impressionistic pieces of Debussy and Ravel for early advanced students. These composers' selected short piano pieces display richly colored sonority through the use of impressionistic techniques such as non-functional harmony (parallel chords and free modulation), exotic setting (e.g. modality, pentatonic and whole-tone scales), ostinato figures, bell-sound imitation, and extended texture. Moreover, personal interpretive elements, such as poetic and folklore references, were incorporated in some piano works of MacDowell, Griffes, Bauer, Scott, and Bax; among them MacDowell and Bax were particularly inspired by Celtic and Nordic materials. Mompou infused Spanish folklores in his individual naïve style. Most importantly, these selected short piano pieces are approachable and attractive to early advanced pianists. These works, as well as other largely undiscovered impressionistic piano character pieces, ought to be a great source of preliminary repertoire as preparation for the music of Debussy and Ravel.
ContributorsChien, Chieh Jenny (Author) / Thompson, Janice Meyer (Thesis advisor) / Hamilton, Robert (Committee member) / Humphreys, Jere (Committee member) / Norton, Kay (Committee member) / Pagano, Caio (Committee member) / Arizona State University (Publisher)
Created2011
Description
Four Souvenirs for Violin and Piano was composed by Paul Schoenfeld (b.1947) in 1990 as a showpiece, spotlighting the virtuosity of both the violin and piano in equal measure. Each movement is a modern interpretation of a folk or popular genre, re- envisioned over intricate jazz harmonies and rhythms. The work was commissioned by violinist Lev Polyakin, who specifically requested some short pieces that could be performed in a local jazz establishment named Night Town in Cleveland, Ohio. The result is a work that is approximately fifteen minutes in length. Schoenfeld is a respected composer in the contemporary classical music community, whose Café Music (1986) for piano trio has recently become a staple of the standard chamber music repertoire. Many of his other works, however, remain in relative obscurity. It is the focus of this document to shed light on at least one other notable composition; Four Souvenirs for Violin and Piano. Among the topics to be discussed regarding this piece are a brief history behind the genesis of this composition, a structural summary of the entire work and each of its movements, and an appended practice guide based on interview and coaching sessions with the composer himself. With this project, I hope to provide a better understanding and appreciation of this work.
ContributorsJanczyk, Kristie Annette (Author) / Ryan, Russell (Thesis advisor) / Campbell, Andrew (Committee member) / Norton, Kay (Committee member) / Arizona State University (Publisher)
Created2015
Description
In a collaborative environment where multiple robots and human beings are expected
to collaborate to perform a task, it becomes essential for a robot to be aware of multiple
agents working in its work environment. A robot must also learn to adapt to
different agents in the workspace and conduct its interaction based on the presence
of these agents. A theoretical framework was introduced which performs interaction
learning from demonstrations in a two-agent work environment, and it is called
Interaction Primitives.
This document is an in-depth description of the new state of the art Python
Framework for Interaction Primitives between two agents in a single as well as multiple
task work environment and extension of the original framework in a work environment
with multiple agents doing a single task. The original theory of Interaction
Primitives has been extended to create a framework which will capture correlation
between more than two agents while performing a single task. The new state of the
art Python framework is an intuitive, generic, easy to install and easy to use python
library which can be applied to use the Interaction Primitives framework in a work
environment. This library was tested in simulated environments and controlled laboratory
environment. The results and benchmarks of this library are available in the
related sections of this document.
to collaborate to perform a task, it becomes essential for a robot to be aware of multiple
agents working in its work environment. A robot must also learn to adapt to
different agents in the workspace and conduct its interaction based on the presence
of these agents. A theoretical framework was introduced which performs interaction
learning from demonstrations in a two-agent work environment, and it is called
Interaction Primitives.
This document is an in-depth description of the new state of the art Python
Framework for Interaction Primitives between two agents in a single as well as multiple
task work environment and extension of the original framework in a work environment
with multiple agents doing a single task. The original theory of Interaction
Primitives has been extended to create a framework which will capture correlation
between more than two agents while performing a single task. The new state of the
art Python framework is an intuitive, generic, easy to install and easy to use python
library which can be applied to use the Interaction Primitives framework in a work
environment. This library was tested in simulated environments and controlled laboratory
environment. The results and benchmarks of this library are available in the
related sections of this document.
ContributorsKumar, Ashish, M.S (Author) / Amor, Hani Ben (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017
Description
Computer Vision as a eld has gone through signicant changes in the last decade.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
ContributorsVenkatesan, Ragav (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017
Description
Historically, music and the experiences of deaf or hard-of-hearing (DHH) individuals have been intertwined in one manner or another. However, music has never ignited as much hope for the “improvement” of the Deaf experience as during the American oralist movement (ca. 1880-1960) which prioritized lip-reading and speaking over the use of sign language. While it is acknowledged that the oralist movement failed to provide the best possible education to many American DHH students and devastated many within the Deaf community, music scholars have continued to cite publications by oralist educators as rationales for the continued development of music programs for DHH students.
This document is an attempt to reframe the role of music during the American oralist movement with a historical account of ways music was recruited as a tool for teaching vocal articulation at schools for the deaf from 1900 to 1960. During this time period, music was recruited simply as a utility to overcome disability and as an aid for assimilating into the hearing world rather than as the rich experiential phenomenon it could have been for the DHH community. My goal is to add this important caveat to the received history of early institutional music education for DHH students. Primary sources include articles published between 1900 and 1956 in The Volta Review, a journal founded by the oralist leader Alexander Graham Bell (1847-1922).
This document is an attempt to reframe the role of music during the American oralist movement with a historical account of ways music was recruited as a tool for teaching vocal articulation at schools for the deaf from 1900 to 1960. During this time period, music was recruited simply as a utility to overcome disability and as an aid for assimilating into the hearing world rather than as the rich experiential phenomenon it could have been for the DHH community. My goal is to add this important caveat to the received history of early institutional music education for DHH students. Primary sources include articles published between 1900 and 1956 in The Volta Review, a journal founded by the oralist leader Alexander Graham Bell (1847-1922).
ContributorsLloyd, Abby Lynn (Author) / Norton, Kay (Thesis advisor) / Gardner, Joshua (Committee member) / Wells, Christopher (Committee member) / Arizona State University (Publisher)
Created2017
Description
With the rise of the Big Data Era, an exponential amount of network data is being generated at an unprecedented rate across a wide-range of high impact micro and macro areas of research---from protein interaction to social networks. The critical challenge is translating this large scale network data into actionable information.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
ContributorsFreitas, Scott (Author) / Tong, Hanghang (Thesis advisor) / Maciejewski, Ross (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
Description
The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos.
The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.
In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.
In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
ContributorsChandakkar, Parag Shridhar (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017
Description
This project includes a recording, composer biographies, performance guides, and composer questionnaires for seven original works commissioned for either the Rogue Trio or Lotus. The members of the Rogue Trio are violinist Kathleen Strahm, saxophonist Justin Rollefson, and pianist Mary Cota. Lotus’s members include Samuel Detweiler, Justin Rollefson, and Kristen Zelenak on saxophone. Both ensembles are based in Tempe, Arizona. All seven original compositions were recorded at Tempest Recording in February of 2018.
The first piece, Four Impersonations (2016), was commissioned by the Rogue Trio and written by Theo Chandler (b.1992) for violin, soprano saxophone and piano. The second piece was written by Spencer Arias (b. 1990) titled He Said There Was No Sound (2015) for violin, alto saxophone, and piano. The final work is titled Cabinet Meeting (2017), composed by Zachary Green (b. 1993) for violin, alto and tenor saxophone, and piano.
The first piece commissioned by Lotus and composed by Spencer Arias is titled As I escape, the water calms (2017) for soprano saxophone, alto saxophone, and tenor saxophone. The second piece was composed by Graham Cohen (b. 1999), titled Introduction and Toccata (2017), written for soprano, alto, and baritone saxophones. The third piece, titled Everything that rises, was written by David “Clay” Mettens (b. 1990) in 2014 for three soprano saxophones. Samuel Detweiler, Justin Rollefson and Tyler Flowers originally commissioned this piece. The final piece commissioned by Lotus was written by Matthew Kennedy (b. 1987) titled Triceratops: tasty grooves for saxophone trio (2017) for alto, tenor, and baritone saxophones.
The first piece, Four Impersonations (2016), was commissioned by the Rogue Trio and written by Theo Chandler (b.1992) for violin, soprano saxophone and piano. The second piece was written by Spencer Arias (b. 1990) titled He Said There Was No Sound (2015) for violin, alto saxophone, and piano. The final work is titled Cabinet Meeting (2017), composed by Zachary Green (b. 1993) for violin, alto and tenor saxophone, and piano.
The first piece commissioned by Lotus and composed by Spencer Arias is titled As I escape, the water calms (2017) for soprano saxophone, alto saxophone, and tenor saxophone. The second piece was composed by Graham Cohen (b. 1999), titled Introduction and Toccata (2017), written for soprano, alto, and baritone saxophones. The third piece, titled Everything that rises, was written by David “Clay” Mettens (b. 1990) in 2014 for three soprano saxophones. Samuel Detweiler, Justin Rollefson and Tyler Flowers originally commissioned this piece. The final piece commissioned by Lotus was written by Matthew Kennedy (b. 1987) titled Triceratops: tasty grooves for saxophone trio (2017) for alto, tenor, and baritone saxophones.
ContributorsRollefson, Justin David (Author) / Creviston, Christopher (Thesis advisor) / Gardner, Joshua (Committee member) / Norton, Kay (Committee member) / Arizona State University (Publisher)
Created2018
Description
Topological methods for data analysis present opportunities for enforcing certain invariances of broad interest in computer vision: including view-point in activity analysis, articulation in shape analysis, and measurement invariance in non-linear dynamical modeling. The increasing success of these methods is attributed to the complementary information that topology provides, as well as availability of tools for computing topological summaries such as persistence diagrams. However, persistence diagrams are multi-sets of points and hence it is not straightforward to fuse them with features used for contemporary machine learning tools like deep-nets. In this paper theoretically well-grounded approaches to develop novel perturbation robust topological representations are presented, with the long-term view of making them amenable to fusion with contemporary learning architectures. The proposed representation lives on a Grassmann manifold and hence can be efficiently used in machine learning pipelines.
The proposed representation.The efficacy of the proposed descriptor was explored on three applications: view-invariant activity analysis, 3D shape analysis, and non-linear dynamical modeling. Favorable results in both high-level recognition performance and improved performance in reduction of time-complexity when compared to other baseline methods are obtained.
The proposed representation.The efficacy of the proposed descriptor was explored on three applications: view-invariant activity analysis, 3D shape analysis, and non-linear dynamical modeling. Favorable results in both high-level recognition performance and improved performance in reduction of time-complexity when compared to other baseline methods are obtained.
ContributorsThopalli, Kowshik (Author) / Turaga, Pavan Kumar (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017