Matching Items (377)
Description
The end of the nineteenth century was an exhilarating and revolutionary era for the flute. This period is the Second Golden Age of the flute, when players and teachers associated with the Paris Conservatory developed what would be considered the birth of the modern flute school. In addition, the founding in 1871 of the Société Nationale de Musique by Camille Saint-Saëns (1835-1921) and Romain Bussine (1830-1899) made possible the promotion of contemporary French composers. The founding of the Société des Instruments à Vent by Paul Taffanel (1844-1908) in 1879 also invigorated a new era of chamber music for wind instruments. Within this groundbreaking environment, Mélanie Hélène Bonis (pen name Mel Bonis) entered the Paris Conservatory in 1876, under the tutelage of César Franck (1822-1890). Many flutists are dismayed by the scarcity of repertoire for the instrument in the Romantic and post-Romantic traditions; they make up for this absence by borrowing the violin sonatas of Gabriel Fauré (1845-1924) and Franck. The flute and piano works of Mel Bonis help to fill this void with music composed originally for flute. Bonis was a prolific composer with over 300 works to her credit, but her works for flute and piano have not been researched or professionally recorded in the United States before the present study. Although virtually unknown today in the American flute community, Bonis's music received much acclaim from her contemporaries and deserves a prominent place in the flutist's repertoire. After a brief biographical introduction, this document examines Mel Bonis's musical style and describes in detail her six works for flute and piano while also offering performance suggestions.
ContributorsDaum, Jenna Elyse (Author) / Buck, Elizabeth (Thesis advisor) / Holbrook, Amy (Committee member) / Micklich, Albie (Committee member) / Schuring, Martin (Committee member) / Norton, Kay (Committee member) / Arizona State University (Publisher)
Created2013
ContributorsMatthews, Eyona (Performer) / Yoo, Katie Jihye (Performer) / Roubison, Ryan (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-25
ContributorsHoeckley, Stephanie (Performer) / Lee, Juhyun (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-24
ContributorsMcClain, Katelyn (Performer) / Buringrud, Deanna (Contributor) / Lee, Juhyun (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-31
ContributorsHur, Jiyoun (Performer) / Lee, Juhyun (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-01
ContributorsZaleski, Kimberly (Contributor) / Kazarian, Trevor (Performer) / Ryan, Russell (Performer) / IN2ATIVE (Performer) / ASU Library. Music Library (Publisher)
Created2018-09-28
ContributorsDelaney, Erin (Performer) / Novak, Gail (Pianist) (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-18
Description
Ensemble learning methods like bagging, boosting, adaptive boosting, stacking have traditionally shown promising results in improving the predictive accuracy in classification. These techniques have recently been widely used in various domains and applications owing to the improvements in computational efficiency and distributed computing advances. However, with the advent of wide variety of applications of machine learning techniques to class imbalance problems, further focus is needed to evaluate, improve and optimize other performance measures such as sensitivity (true positive rate) and specificity (true negative rate) in classification. This thesis demonstrates a novel approach to evaluate and optimize the performance measures (specifically sensitivity and specificity) using ensemble learning methods for classification that can be especially useful in class imbalanced datasets. In this thesis, ensemble learning methods (specifically bagging and boosting) are used to optimize the performance measures (sensitivity and specificity) on a UC Irvine (UCI) 130 hospital diabetes dataset to predict if a patient will be readmitted to the hospital based on various feature vectors. From the experiments conducted, it can be empirically concluded that, by using ensemble learning methods, although accuracy does improve to some margin, both sensitivity and specificity are optimized significantly and consistently over different cross validation approaches. The implementation and evaluation has been done on a subset of the large UCI 130 hospital diabetes dataset. The performance measures of ensemble learners are compared to the base machine learning classification algorithms such as Naive Bayes, Logistic Regression, k Nearest Neighbor, Decision Trees and Support Vector Machines.
ContributorsBahl, Neeraj Dharampal (Author) / Bansal, Ajay (Thesis advisor) / Amresh, Ashish (Committee member) / Bansal, Srividya (Committee member) / Arizona State University (Publisher)
Created2017
Description
ABSTRACT Many musicians, both amateur and professional alike, are continuously seeking to expand and explore their performance literature and repertory. Introducing new works into the standard repertory is an exciting endeavor for any active musician. Establishing connections, commissioning new works, and collaborating on performances can all work together toward the acceptance and success of a composer's music within an instrument community. For the flute, one such composer is Daniel Dorff (b. 1956). Dorff, a Philadelphia-based composer, has written for symphony orchestra, clarinet, contrabassoon, and others; however, his award-winning works for flute and piccolo are earning him much recognition. He has written works for such illustrious flutists as Mimi Stillman, Walfrid Kujala, and Gary Schocker; his flute works have been recorded by Laurel Zucker, Pamela Youngblood and Lois Bliss Herbine; and his pieces have been performed and premiered at each of the National Flute Association Conventions from 2004 to 2009. Despite this success, little has been written about Dorff's life, compositional style, and contributions to the flute repertory. In order to further promote the flute works of Daniel Dorff, the primary focus of this study is the creation of a compact disc recording of Dorff's most prominent works for flute: April Whirlwind, 9 Walks Down 7th Avenue, both for flute and piano, and Nocturne Caprice for solo flute. In support of this recording, the study also provides biographical information regarding Daniel Dorff, discusses his compositional methods and ideology, and presents background information, description, and performance notes for each piece. Interviews with Daniel Dorff regarding biographical and compositional details serve as the primary source for this document. Suggestions for the performance of the three flute works were gathered through interviews with prominent flutists who have studied and performed Dorff's pieces. Additional performance suggestions for Nocturne Caprice were gathered through a coaching session between the author and the composer. This project is meant to promote the flute works of Daniel Dorff and to help establish their role in the standard flute repertory.
ContributorsRich, Angela Marie (Contributor) / Novak, Gail (Pianist) (Performer) / Buck, Elizabeth Y (Thesis advisor) / Hill, Gary W. (Committee member) / Holbrook, Amy (Committee member) / Schuring, Martin (Committee member) / Arizona State University (Publisher)
Created2010
ContributorsChen, Zuoquan (Performer) / Jiang, Zhou (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-02