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ContributorsShi, Ge (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-25
ContributorsShatuho, Kristina (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-27
ContributorsCarlisi, Daniel (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-07
Description
Yannis Constantinidis was the last of the handful of composers referred to collectively as the Greek National School. The members of this group strove to create a distinctive national style for Greece, founded upon a synthesis of Western compositional idioms with melodic, rhyhmic, and modal features of their local folk traditions. Constantinidis particularly looked to the folk melodies of his native Asia Minor and the nearby Dodecanese Islands. His musical output includes operettas, musical comedies, orchestral works, chamber and vocal music, and much piano music, all of which draws upon folk repertories for thematic material. The present essay examines how he incorporates this thematic material in his piano compositions, written between 1943 and 1971, with a special focus on the 22 Songs and Dances from the Dodecanese. In general, Constantinidis's pianistic style is expressed through miniature pieces in which the folk tunes are presented mostly intact, but embedded in accompaniment based in early twentieth-century modal harmony. Following the dictates of the founding members of the Greek National School, Manolis Kalomiris and Georgios Lambelet, the modal basis of his harmonic vocabulary is firmly rooted in the characteristics of the most common modes of Greek folk music. A close study of his 22 Songs and Dances from the Dodecanese not only offers a valuable insight into his harmonic imagination, but also demonstrates how he subtly adapts his source melodies. This work also reveals his care in creating a musical expression of the words of the original folk songs, even in purely instrumental compositon.
ContributorsSavvidou, Dina (Author) / Hamilton, Robert (Thesis advisor) / Little, Bliss (Committee member) / Meir, Baruch (Committee member) / Thompson, Janice M (Committee member) / Arizona State University (Publisher)
Created2011
Description
This paper describes six representative works by twentieth-century Chinese composers: Jian-Zhong Wang, Er-Yao Lin, Yi-Qiang Sun, Pei-Xun Chen, Ying-Hai Li, and Yi Chen, which are recorded by the author on the CD. The six pieces selected for the CD all exemplify traits of Nationalism, with or without Western influences. Of the six works on the CD, two are transcriptions of the Han Chinese folk-like songs, one is a composition in the style of the Uyghur folk music, two are transcriptions of traditional Chinese instrumental music dating back to the eighteenth century, and one is an original composition in a contemporary style using folk materials. Two of the composers, who studied in the United States, were strongly influenced by Western compositional style. The other four, who did not study abroad, retained traditional Chinese style in their compositions. The pianistic level of difficulty in these six pieces varies from intermediate to advanced level. This paper includes biographical information for the six composers, background information on the compositions, and a brief analysis of each work. The author was exposed to these six pieces growing up, always believing that they are beautiful and deserve to be appreciated. When the author came to the United States for her studies, she realized that Chinese compositions, including these six pieces, were not sufficiently known to her peers. This recording and paper are offered in the hopes of promoting a wider familiarity with Chinese music and culture.
ContributorsLuo, Yali, D.M.A (Author) / Hamilton, Robert (Thesis advisor) / Campbell, Andrew (Committee member) / Pagano, Caio (Committee member) / Cosand, Walter (Committee member) / Rogers, Rodney (Committee member) / Arizona State University (Publisher)
Created2012
Description
The purpose of this project was to examine the lives and solo piano works of four members of the early generation of female composers in Taiwan. These four women were born between 1950 and 1960, began to appear on the Taiwanese musical scene after 1980, and were still active as composers at the time of this study. They include Fan-Ling Su (b. 1955), Hwei-Lee Chang (b. 1956), Shyh-Ji Pan-Chew (b. 1957), and Kwang-I Ying (b. 1960). Detailed biographical information on the four composers is presented and discussed. In addition, the musical form and features of all solo piano works at all levels by the four composers are analyzed, and the musical characteristics of each composer's work are discussed. The biography of a fifth composer, Wei-Ho Dai (b. 1950), is also discussed but is placed in the Appendices because her piano music could not be located. This research paper is presented in six chapters: (1) Prologue; the life and music of (2) Fan-Ling Su, (3) Hwei-Lee Chang, (4) Shyh-Ji Pan-Chew, and (5) Kwang-I Ying; and (6) Conclusion. The Prologue provides an overview of the development of Western classical music in Taiwan, a review of extant literature on the selected composers and their music, and the development of piano music in Taiwan. The Conclusion is comprised of comparisons of the four composers' music, including their personal interests and preferences as exhibited in their music. For example, all of the composers have used atonality in their music. Two of the composers, Fan-Ling Su and Kwang-I Ying, openly apply Chinese elements in their piano works, while Hwei-Lee Chang tries to avoid direct use of the Chinese pentatonic scale. The piano works of Hwei-Lee Chang and Shyh-Ji Pan-Chew are chromatic and atonal, and show an economical usage of material. Biographical information on Wei-Ho Dai and an overview of Taiwanese history are presented in the Appendices.
ContributorsWang, Jinding (Author) / Pagano, Caio (Thesis advisor) / Campbell, Andrew (Committee member) / Humphreys, Jere T. (Committee member) / Meyer-Thompson, Janice (Committee member) / Norton, Kay (Committee member) / Arizona State University (Publisher)
Created2011
ContributorsShi, Zhan (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-26
Description
Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning of the relevant patterns This dissertation proposes TS representations and methods for supervised TS analysis. The approaches combine new representations that handle translations and dilations of patterns with bag-of-features strategies and tree-based ensemble learning. This provides flexibility in handling time-warped patterns in a computationally efficient way. The ensemble learners provide a classification framework that can handle high-dimensional feature spaces, multiple classes and interaction between features. The proposed representations are useful for classification and interpretation of the TS data of varying complexity. The first contribution handles the problem of time warping with a feature-based approach. An interval selection and local feature extraction strategy is proposed to learn a bag-of-features representation. This is distinctly different from common similarity-based time warping. This allows for additional features (such as pattern location) to be easily integrated into the models. The learners have the capability to account for the temporal information through the recursive partitioning method. The second contribution focuses on the comprehensibility of the models. A new representation is integrated with local feature importance measures from tree-based ensembles, to diagnose and interpret time intervals that are important to the model. Multivariate time series (MTS) are especially challenging because the input consists of a collection of TS and both features within TS and interactions between TS can be important to models. Another contribution uses a different representation to produce computationally efficient strategies that learn a symbolic representation for MTS. Relationships between the multiple TS, nominal and missing values are handled with tree-based learners. Applications such as speech recognition, medical diagnosis and gesture recognition are used to illustrate the methods. Experimental results show that the TS representations and methods provide better results than competitive methods on a comprehensive collection of benchmark datasets. Moreover, the proposed approaches naturally provide solutions to similarity analysis, predictive pattern discovery and feature selection.
ContributorsBaydogan, Mustafa Gokce (Author) / Runger, George C. (Thesis advisor) / Atkinson, Robert (Committee member) / Gel, Esma (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2012
Description
The amount of time series data generated is increasing due to the integration of sensor technologies with everyday applications, such as gesture recognition, energy optimization, health care, video surveillance. The use of multiple sensors simultaneously
for capturing different aspects of the real world attributes has also led to an increase in dimensionality from uni-variate to multi-variate time series. This has facilitated richer data representation but also has necessitated algorithms determining similarity between two multi-variate time series for search and analysis.
Various algorithms have been extended from uni-variate to multi-variate case, such as multi-variate versions of Euclidean distance, edit distance, dynamic time warping. However, it has not been studied how these algorithms account for asynchronous in time series. Human gestures, for example, exhibit asynchrony in their patterns as different subjects perform the same gesture with varying movements in their patterns at different speeds. In this thesis, we propose several algorithms (some of which also leverage metadata describing the relationships among the variates). In particular, we present several techniques that leverage the contextual relationships among the variates when measuring multi-variate time series similarities. Based on the way correlation is leveraged, various weighing mechanisms have been proposed that determine the importance of a dimension for discriminating between the time series as giving the same weight to each dimension can led to misclassification. We next study the robustness of the considered techniques against different temporal asynchronies, including shifts and stretching.
Exhaustive experiments were carried on datasets with multiple types and amounts of temporal asynchronies. It has been observed that accuracy of algorithms that rely on data to discover variate relationships can be low under the presence of temporal asynchrony, whereas in case of algorithms that rely on external metadata, robustness against asynchronous distortions tends to be stronger. Specifically, algorithms using external metadata have better classification accuracy and cluster separation than existing state-of-the-art work, such as EROS, PCA, and naive dynamic time warping.
for capturing different aspects of the real world attributes has also led to an increase in dimensionality from uni-variate to multi-variate time series. This has facilitated richer data representation but also has necessitated algorithms determining similarity between two multi-variate time series for search and analysis.
Various algorithms have been extended from uni-variate to multi-variate case, such as multi-variate versions of Euclidean distance, edit distance, dynamic time warping. However, it has not been studied how these algorithms account for asynchronous in time series. Human gestures, for example, exhibit asynchrony in their patterns as different subjects perform the same gesture with varying movements in their patterns at different speeds. In this thesis, we propose several algorithms (some of which also leverage metadata describing the relationships among the variates). In particular, we present several techniques that leverage the contextual relationships among the variates when measuring multi-variate time series similarities. Based on the way correlation is leveraged, various weighing mechanisms have been proposed that determine the importance of a dimension for discriminating between the time series as giving the same weight to each dimension can led to misclassification. We next study the robustness of the considered techniques against different temporal asynchronies, including shifts and stretching.
Exhaustive experiments were carried on datasets with multiple types and amounts of temporal asynchronies. It has been observed that accuracy of algorithms that rely on data to discover variate relationships can be low under the presence of temporal asynchrony, whereas in case of algorithms that rely on external metadata, robustness against asynchronous distortions tends to be stronger. Specifically, algorithms using external metadata have better classification accuracy and cluster separation than existing state-of-the-art work, such as EROS, PCA, and naive dynamic time warping.
ContributorsGarg, Yash (Author) / Candan, Kasim Selcuk (Thesis advisor) / Chowell-Punete, Gerardo (Committee member) / Tong, Hanghang (Committee member) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Arizona State University (Publisher)
Created2015
ContributorsLee, Eunhwa (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-17