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- All Subjects: Violin and piano music
ContributorsChang, Ruihong (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-29
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
ContributorsWhite, Aaron (Performer) / Kim, Olga (Performer) / Hammond, Marinne (Performer) / Shaner, Hayden (Performer) / Yoo, Katie (Performer) / Shoemake, Crista (Performer) / Gebe, Vladimir, 1987- (Performer) / Wills, Grace (Performer) / McKinch, Riley (Performer) / Freshmen Four (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-27
ContributorsRosenfeld, Albor (Performer) / Pagano, Caio, 1940- (Performer) / ASU Library. Music Library (Publisher)
Created2018-10-03
ContributorsCao, Yuchen (Performer) / Chen, Sicong (Performer) / Soberano, Chino (Performer) / Nam, Michelle (Performer) / Collins, Clarice (Performer) / Witt, Juliana (Performer) / Liu, Jingting (Performer) / Chen, Neilson (Performer) / Zhang, Aihua (Performer) / Jiang, Zhou (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-25
ContributorsMcLin, Katherine (Performer) / Campbell, Andrew (Pianist) (Performer) / Ericson, John Q. (John Quincy), 1962- (Performer) / McLin/Campbell Duo (Performer) / ASU Library. Music Library (Publisher)
Created2018-09-23
Description
Samuel Máynez Prince (1886-1966), was a prolific and important Mexican musician. Prince’s musical style followed the trends of the nineteenth-century salon music genre. His compositions include lullabies, songs, dances, marches, mazurkas, waltzes, and revolutionary anthems. Prince’s social status and performances in the famed Café Colón in Mexico City increased his popularity among high-ranking political figures during the time of the Mexican Revolution as well as his status in the Mexican music scene.
Unfortunately there is virtually no existing scholarship on Prince and even basic information regarding his life and works is not readily available. The lack of organization of the manuscript scores and the absence of dates of his works has further pushed the composer into obscurity. An investigation therefore was necessary in order to explore the neglected aspects of the life and works of Prince as a violinist and composer. This document is the result of such an investigation by including extensive new biographical information, as well as the first musical analysis and edition of the complete recovered works for violin and piano.
In order to fill the gaps present in the limited biographical information regarding Prince’s life, investigative research was conducted in Mexico City. Information was drawn from archives of the composer’s grandchildren, the Palacio de Bellas Artes, the Conservatorio Nacional de Música de México, and the Orquesta Sinfónica Nacional. The surviving relatives provided first-hand details on events in the composer’s life; one also offered the researcher access to their personal archive including, important life documents, photographs, programs from concert performances, and manuscript scores of the compositions. Establishing connections with the relatives also led the researcher to examining the violins owned and used by the late violinist/composer.
This oral history approach led to new and updated information, including the revival of previously unpublished music for violin and piano. These works are here compiled in an edition that will give students, teachers, and music-lovers access to this unknown repertoire. Finally, this research seeks to promote the beauty and nuances of Mexican salon music, and the complete works for violin and piano of Samuel Máynez Prince in particular.
Unfortunately there is virtually no existing scholarship on Prince and even basic information regarding his life and works is not readily available. The lack of organization of the manuscript scores and the absence of dates of his works has further pushed the composer into obscurity. An investigation therefore was necessary in order to explore the neglected aspects of the life and works of Prince as a violinist and composer. This document is the result of such an investigation by including extensive new biographical information, as well as the first musical analysis and edition of the complete recovered works for violin and piano.
In order to fill the gaps present in the limited biographical information regarding Prince’s life, investigative research was conducted in Mexico City. Information was drawn from archives of the composer’s grandchildren, the Palacio de Bellas Artes, the Conservatorio Nacional de Música de México, and the Orquesta Sinfónica Nacional. The surviving relatives provided first-hand details on events in the composer’s life; one also offered the researcher access to their personal archive including, important life documents, photographs, programs from concert performances, and manuscript scores of the compositions. Establishing connections with the relatives also led the researcher to examining the violins owned and used by the late violinist/composer.
This oral history approach led to new and updated information, including the revival of previously unpublished music for violin and piano. These works are here compiled in an edition that will give students, teachers, and music-lovers access to this unknown repertoire. Finally, this research seeks to promote the beauty and nuances of Mexican salon music, and the complete works for violin and piano of Samuel Máynez Prince in particular.
ContributorsEkenes, Spencer Arvin (Author) / McLin, Katherine (Thesis advisor) / Feisst, Sabine (Committee member) / Jiang, Danwen (Committee member) / Arizona State University (Publisher)
Created2016
Description
Feature learning and the discovery of nonlinear variation patterns in high-dimensional data is an important task in many problem domains, such as imaging, streaming data from sensors, and manufacturing. This dissertation presents several methods for learning and visualizing nonlinear variation in high-dimensional data. First, an automated method for discovering nonlinear variation patterns using deep learning autoencoders is proposed. The approach provides a functional mapping from a low-dimensional representation to the original spatially-dense data that is both interpretable and efficient with respect to preserving information. Experimental results indicate that deep learning autoencoders outperform manifold learning and principal component analysis in reproducing the original data from the learned variation sources.
A key issue in using autoencoders for nonlinear variation pattern discovery is to encourage the learning of solutions where each feature represents a unique variation source, which we define as distinct features. This problem of learning distinct features is also referred to as disentangling factors of variation in the representation learning literature. The remainder of this dissertation highlights and provides solutions for this important problem.
An alternating autoencoder training method is presented and a new measure motivated by orthogonal loadings in linear models is proposed to quantify feature distinctness in the nonlinear models. Simulated point cloud data and handwritten digit images illustrate that standard training methods for autoencoders consistently mix the true variation sources in the learned low-dimensional representation, whereas the alternating method produces solutions with more distinct patterns.
Finally, a new regularization method for learning distinct nonlinear features using autoencoders is proposed. Motivated in-part by the properties of linear solutions, a series of learning constraints are implemented via regularization penalties during stochastic gradient descent training. These include the orthogonality of tangent vectors to the manifold, the correlation between learned features, and the distributions of the learned features. This regularized learning approach yields low-dimensional representations which can be better interpreted and used to identify the true sources of variation impacting a high-dimensional feature space. Experimental results demonstrate the effectiveness of this method for nonlinear variation pattern discovery on both simulated and real data sets.
A key issue in using autoencoders for nonlinear variation pattern discovery is to encourage the learning of solutions where each feature represents a unique variation source, which we define as distinct features. This problem of learning distinct features is also referred to as disentangling factors of variation in the representation learning literature. The remainder of this dissertation highlights and provides solutions for this important problem.
An alternating autoencoder training method is presented and a new measure motivated by orthogonal loadings in linear models is proposed to quantify feature distinctness in the nonlinear models. Simulated point cloud data and handwritten digit images illustrate that standard training methods for autoencoders consistently mix the true variation sources in the learned low-dimensional representation, whereas the alternating method produces solutions with more distinct patterns.
Finally, a new regularization method for learning distinct nonlinear features using autoencoders is proposed. Motivated in-part by the properties of linear solutions, a series of learning constraints are implemented via regularization penalties during stochastic gradient descent training. These include the orthogonality of tangent vectors to the manifold, the correlation between learned features, and the distributions of the learned features. This regularized learning approach yields low-dimensional representations which can be better interpreted and used to identify the true sources of variation impacting a high-dimensional feature space. Experimental results demonstrate the effectiveness of this method for nonlinear variation pattern discovery on both simulated and real data sets.
ContributorsHoward, Phillip (Author) / Runger, George C. (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Mirchandani, Pitu (Committee member) / Apley, Daniel (Committee member) / Arizona State University (Publisher)
Created2016