ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 10 of 136
Description
This document is intended to show the various kinds of stylistically appropriate melodic and rhythmic ornamentation that can be used in the improvisation of the Sarabandes by J.S. Bach. Traditional editions of Bach's and other Baroque-era keyboard works have reflected evolving historical trends. The historical performance movement and other attempts to "clean up" pre-1950s romanticized performances have greatly limited the freedom and experimentation that was the original intention of these dances. Prior to this study, few ornamented editions of these works have been published. Although traditional practices do not necessarily encourage classical improvisation in performance I argue that manipulation of the melodic and rhythmic layers over the established harmonic progressions will not only provide diversity within the individual dance movements, but also further engage the ears of the performer and listener which encourages further creative exploration. I will focus this study on the ornamentation of all six Sarabandes from J.S. Bach's French Suites and show how various types of melodic and rhythmic variation can provide aurally pleasing alternatives to the composed score without disrupting the harmonic fluency. The author intends this document to be used as a pedagogical tool and the fully ornamented Sarabandes from J.S. Bach's French Suites are included with this document.
ContributorsOakley, Ashley (Author) / Meir, Baruch (Thesis advisor) / Campbell, Andrew (Committee member) / Norton, Kay (Committee member) / Pagano, Caio (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.
ContributorsChattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
Currently, to interact with computer based systems one needs to learn the specific interface language of that system. In most cases, interaction would be much easier if it could be done in natural language. For that, we will need a module which understands natural language and automatically translates it to the interface language of the system. NL2KR (Natural language to knowledge representation) v.1 system is a prototype of such a system. It is a learning based system that learns new meanings of words in terms of lambda-calculus formulas given an initial lexicon of some words and their meanings and a training corpus of sentences with their translations. As a part of this thesis, we take the prototype NL2KR v.1 system and enhance various components of it to make it usable for somewhat substantial and useful interface languages. We revamped the lexicon learning components, Inverse-lambda and Generalization modules, and redesigned the lexicon learning algorithm which uses these components to learn new meanings of words. Similarly, we re-developed an inbuilt parser of the system in Answer Set Programming (ASP) and also integrated external parser with the system. Apart from this, we added some new rich features like various system configurations and memory cache in the learning component of the NL2KR system. These enhancements helped in learning more meanings of the words, boosted performance of the system by reducing the computation time by a factor of 8 and improved the usability of the system. We evaluated the NL2KR system on iRODS domain. iRODS is a rule-oriented data system, which helps in managing large set of computer files using policies. This system provides a Rule-Oriented interface langauge whose syntactic structure is like any procedural programming language (eg. C). However, direct translation of natural language (NL) to this interface language is difficult. So, for automatic translation of NL to this language, we define a simple intermediate Policy Declarative Language (IPDL) to represent the knowledge in the policies, which then can be directly translated to iRODS rules. We develop a corpus of 100 policy statements and manually translate them to IPDL langauge. This corpus is then used for the evaluation of NL2KR system. We performed 10 fold cross validation on the system. Furthermore, using this corpus, we illustrate how different components of our NL2KR system work.
ContributorsKumbhare, Kanchan Ravishankar (Author) / Baral, Chitta (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2013
Description
The purpose of the paper is to outline the process that was used to write a reduction for Henry Brant's Concerto for Alto Saxophone and Orchestra, to describe the improvements in saxophone playing since the premiere of the piece, and to demonstrate the necessity of having a reduction in the process of learning a concerto. The Concerto was inspired by internationally known saxophonist, Sigurd Rascher, who demonstrated for Brant the extent of his abilities on the saxophone. These abilities included use of four-octave range and two types of extended techniques: slap-tonguing and flutter-tonguing. Brant incorporated all three elements in his Concerto, and believed that only Rascher had the command over the saxophone needed to perform the piece. To prevent the possibility of an unsuccessful performance, Brant chose to make the piece unavailable to saxophonists by leaving the Concerto without a reduction. Subsequently, there were no performances of this piece between 1953 and 2001. In 2011, the two directors of Brant's Estate decided to allow for a reduction to be written for the piece so that it would become more widely available to saxophonists.
ContributorsAmes, Elizabeth (Pianist) (Author) / Ryan, Russell (Thesis advisor) / Levy, Benjamin (Committee member) / Hill, Gary (Committee member) / Campbell, Andrew (Committee member) / Arizona State University (Publisher)
Created2013
Description
The teaching of singing remained remarkably stable until, at the end of the twentieth century, advances in the understanding of voice science stimulated dramatic changes in approach to vocal pedagogy. Previously, the technology needed to accurately measure physiologic change within the larynx and breath-support musculature during the process of singing simply did not exist. Any prior application of scientific study to the voice was based primarily upon auditory evaluation, rather than objective data accumulation and assessment. After a centuries-long history, within a span of twenty years, vocal pedagogy evolved from an approach solely derived from subjective, auditory evidence to an application grounded in scientific data. By means of analysis of significant publications by Richard Miller, Robert Sataloff, and Ingo Titze, as well as articles from The Journal of Singing and The Journal of Voice, I establish a baseline of scientific knowledge and pedagogic practice ca. 1980. Analysis and comparison of a timeline of advancement in scientific insight and the discussion of science in pedagogical texts, 1980-2000, reveal the extent to which voice teachers have dramatically changed their method of instruction. I posit that voice pedagogy has undergone a fundamental change, from telling the student only what to do, via auditory demonstration and visual imagery, to validating with scientific data how and why students should change their vocal approach. The consequence of this dramatic pedagogic evolution has produced singers who comprehend more fully the science of their art.
ContributorsVelarde, Rachel (Author) / Doan, Jerry (Thesis advisor) / Campbell, Andrew (Committee member) / Solis, Theodore (Committee member) / Elgar Kopta, Anne (Committee member) / Britton, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
The Sonata for Violin and Piano in E-flat Major, Op. 18 (1888), was the last major work of chamber music by Richard Strauss (1864-1949). Although for only two instruments, the Sonata reflects Strauss's growing interest in symphonic writing both in his tone poems and orchestral songs, anticipating his style of orchestration and his expressive use of tone colors. This study examines instances of orchestral writing in the piano and makes suggestions for their performance. An overview of Strauss's compositions, from his early chamber music to the `heroic' symphonic works, places the Sonata in context. An analytical description of each of the Sonata's three movements shows the structure and content of this large work and provides the framework for examination of the orchestral effects in the piano. Comparison of excerpts from the Sonata with passages from Strauss's orchestral writing in Don Juan (1889), "Cäcilie," "Morgen!," and "Lied der Frauen" leads to suggestions for the collaborative pianist of ways to re-create the various orchestral effects.
ContributorsNixon, Thomas (Pianist) (Author) / Ryan, Russell R (Thesis advisor) / Campbell, Andrew (Committee member) / FitzPatrick, Carole (Committee member) / Hamilton, Robert (Committee member) / Holbrook, Amy (Committee member) / Arizona State University (Publisher)
Created2014
Description
Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture Structure Analysis is the process of studying the structure present in the textures. This structure can be expressed in terms of perceived regularity. Our human visual system (HVS) uses the perceived regularity as one of the important pre-attentive cues in low-level image understanding. Similar to the HVS, image processing and computer vision systems can make fast and efficient decisions if they can quantify this regularity automatically. In this work, the problem of quantifying the degree of perceived regularity when looking at an arbitrary texture is introduced and addressed. One key contribution of this work is in proposing an objective no-reference perceptual texture regularity metric based on visual saliency. Other key contributions include an adaptive texture synthesis method based on texture regularity, and a low-complexity reduced-reference visual quality metric for assessing the quality of synthesized textures. In order to use the best performing visual attention model on textures, the performance of the most popular visual attention models to predict the visual saliency on textures is evaluated. Since there is no publicly available database with ground-truth saliency maps on images with exclusive texture content, a new eye-tracking database is systematically built. Using the Visual Saliency Map (VSM) generated by the best visual attention model, the proposed texture regularity metric is computed. The proposed metric is based on the observation that VSM characteristics differ between textures of differing regularity. The proposed texture regularity metric is based on two texture regularity scores, namely a textural similarity score and a spatial distribution score. In order to evaluate the performance of the proposed regularity metric, a texture regularity database called RegTEX, is built as a part of this work. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score (MOS) for the perceived regularity of textures. The proposed method is also shown to be robust to geometric and photometric transformations and outperforms some of the popular texture regularity metrics in predicting the perceived regularity. The impact of the proposed metric to improve the performance of many image-processing applications is also presented. The influence of the perceived texture regularity on the perceptual quality of synthesized textures is demonstrated through building a synthesized textures database named SynTEX. It is shown through subjective testing that textures with different degrees of perceived regularities exhibit different degrees of vulnerability to artifacts resulting from different texture synthesis approaches. This work also proposes an algorithm for adaptively selecting the appropriate texture synthesis method based on the perceived regularity of the original texture. A reduced-reference texture quality metric for texture synthesis is also proposed as part of this work. The metric is based on the change in perceived regularity and the change in perceived granularity between the original and the synthesized textures. The perceived granularity is quantified through a new granularity metric that is proposed in this work. It is shown through subjective testing that the proposed quality metric, using just 2 parameters, has a strong correlation with the MOS for the fidelity of synthesized textures and outperforms the state-of-the-art full-reference quality metrics on 3 different texture databases. Finally, the ability of the proposed regularity metric in predicting the perceived degradation of textures due to compression and blur artifacts is also established.
ContributorsVaradarajan, Srenivas (Author) / Karam, Lina J (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Li, Baoxin (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
Description
This qualitative study examines the major changes in relationship closeness of married couples when one spouse acquires a vision disability. Turning Points analysis and Retrospective Interview Technique (RIT) were utilized which required participants to plot their relational journey on a graph after the onset of the disability. A sample of 32 participants generating 100 unique turning points and 32 RIT graphs lent in-depth insight into the less explored area of the impact of a visual disability on marital relationships. A constant comparison method employed for the analysis of these turning points revealed six major categories, which include Change in Relational Dynamics, Realization of the Disability, Regaining Normality in Life, Resilience, Reactions to Assistance, and Dealing with the Disability. These turning points differ in terms of their positive or negative impact on the relational closeness between partners. In addition, the 32 individual RIT graphs were also analyzed and were grouped into four categories based on visual similarity, which include Erratic Relational Restoration, Erratic Relational Increase, Consistent Closeness and Gradual Relational Increase. Results provide theoretical contributions to disability and marriage literature. Implications for the application of turning points to the study of post-disability marital relationships are also discussed, and research directions identified.
ContributorsBhagchandani, Bhoomika (Author) / Kassing, Jeffrey W. (Thesis advisor) / Kelley, Douglas L. (Committee member) / Fisher, Carla L. (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2014
Description
In many fields one needs to build predictive models for a set of related machine learning tasks, such as information retrieval, computer vision and biomedical informatics. Traditionally these tasks are treated independently and the inference is done separately for each task, which ignores important connections among the tasks. Multi-task learning aims at simultaneously building models for all tasks in order to improve the generalization performance, leveraging inherent relatedness of these tasks. In this thesis, I firstly propose a clustered multi-task learning (CMTL) formulation, which simultaneously learns task models and performs task clustering. I provide theoretical analysis to establish the equivalence between the CMTL formulation and the alternating structure optimization, which learns a shared low-dimensional hypothesis space for different tasks. Then I present two real-world biomedical informatics applications which can benefit from multi-task learning. In the first application, I study the disease progression problem and present multi-task learning formulations for disease progression. In the formulations, the prediction at each point is a regression task and multiple tasks at different time points are learned simultaneously, leveraging the temporal smoothness among the tasks. The proposed formulations have been tested extensively on predicting the progression of the Alzheimer's disease, and experimental results demonstrate the effectiveness of the proposed models. In the second application, I present a novel data-driven framework for densifying the electronic medical records (EMR) to overcome the sparsity problem in predictive modeling using EMR. The densification of each patient is a learning task, and the proposed algorithm simultaneously densify all patients. As such, the densification of one patient leverages useful information from other patients.
ContributorsZhou, Jiayu (Author) / Ye, Jieping (Thesis advisor) / Mittelmann, Hans (Committee member) / Li, Baoxin (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2014