Matching Items (10)
Description
This research paper focuses on selected recordings of the Etudes of Claude Debussy. It provides a comparative study of these recordings.
There are some dissertations on the topic of Debussy’s Etudes. Most of them are about performance-related aspects such as fingerings, pedaling, or technical guidelines. Some of the dissertations examine compositional analyses, discussing harmony, texture, rhythmic structure, motivic development, etc. There also is a dissertation that makes a comparative study of the etude genre in Chopin and Debussy. Since there is no research yet on the recordings of Debussy’s Etudes, this may be a meaningful contribution to research. Debussy’s Douze Études are technically difficult to play, but the technical problems are always subordinated to musical beauty and variety in this work. This research is concerned with the sound of the music as achieved by a variety of performers.
Nine representative pianists from various schools and traditions are chosen: Michel Béroff, Aldo Ciccolini, Walter Cosand, Walter Gieseking, Werner Haas, Yvonne Loriod, Jean-Yves Thibaudet, Mitsuko Uchida and Yevgeny Yontov. In this project, the focus is on listening to the selected recordings, making comparisons and summarizing certain performance-related aspects of them. Each etude is discussed individually in order to make a comprehensive study of different aspects of the selected recordings. In the last chapter of this paper, conclusions are drawn about the different performance features of the pianists examined according to previous analyses.
This research seeks to encourage performances of Debussy’s Etudes, to aid pianists in obtaining interpretative ideas from the different recordings and finally to benefit their own performances.
There are some dissertations on the topic of Debussy’s Etudes. Most of them are about performance-related aspects such as fingerings, pedaling, or technical guidelines. Some of the dissertations examine compositional analyses, discussing harmony, texture, rhythmic structure, motivic development, etc. There also is a dissertation that makes a comparative study of the etude genre in Chopin and Debussy. Since there is no research yet on the recordings of Debussy’s Etudes, this may be a meaningful contribution to research. Debussy’s Douze Études are technically difficult to play, but the technical problems are always subordinated to musical beauty and variety in this work. This research is concerned with the sound of the music as achieved by a variety of performers.
Nine representative pianists from various schools and traditions are chosen: Michel Béroff, Aldo Ciccolini, Walter Cosand, Walter Gieseking, Werner Haas, Yvonne Loriod, Jean-Yves Thibaudet, Mitsuko Uchida and Yevgeny Yontov. In this project, the focus is on listening to the selected recordings, making comparisons and summarizing certain performance-related aspects of them. Each etude is discussed individually in order to make a comprehensive study of different aspects of the selected recordings. In the last chapter of this paper, conclusions are drawn about the different performance features of the pianists examined according to previous analyses.
This research seeks to encourage performances of Debussy’s Etudes, to aid pianists in obtaining interpretative ideas from the different recordings and finally to benefit their own performances.
ContributorsJiang, Yuan (Author) / Cosand, Walter (Thesis advisor) / Rogers, Rodney (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2018
ContributorsMartin, Aubrey (Performer) / Olson, Erik (Performer) / Lineweaver, Andrew (Performer) / Borden, Brianne (Performer) / Novak, Gail (Pianist) (Performer) / Jiang, Yuan (Performer) / Amrein, Brielle (Performer) / Chen, Po Hsun (Performer) / Hur, Jiyoun (Performer) / Lee, Juhyun (Performer) / DiBarry, Michael (Performer) / Soberano, Chino (Performer) / Hankins, Kim (Performer) / Son, Jennifer (Performer) / ASU Library. Music Library (Publisher)
Created2017-11-29
ContributorsJiang, Yuan (Performer) / ASU Library. Music Library (Publisher)
Created2014-04-14
ContributorsDarveaux, John (Performer) / Beek, Helena van (Performer) / Jennifer, Ostrowski (Performer) / Tappendorf, Rebecca (Performer) / Kim, Bora (Performer) / Stefanski, Josh (Performer) / Jiang, Yuan (Performer) / Bates, Carly (Performer) / Weinland, Kandi (Performer) / Koehler, Sarah (Performer) / Ye, Qingqing (Performer) / Price, Mary (Performer) / Huish, Sarah (Performer) / Hontz, Joshua (Performer) / Li, Tian (Performer) / ASU Library. Music Library (Publisher)
Created2014-04-15
ContributorsJiang, Yuan (Performer) / ASU Library. Music Library (Publisher)
Created2017-01-26
ContributorsJiang, Yuan (Performer) / ASU Library. Music Library (Publisher)
Created2015-11-13
ContributorsJiang, Yuan (Performer) / Yun, Ahyeon (Performer) / ASU Library. Music Library (Publisher)
Created2013-11-13
ContributorsJiang, Yuan (Performer) / ASU Library. Music Library (Publisher)
Created2018-02-12
ContributorsJiang, Yuan (Performer) / ASU Library. Music Library (Publisher)
Created2017-11-30
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Description
Background
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
ContributorsYuan, Lei (Author) / Woodard, Alexander (Author) / Ji, Shuiwang (Author) / Jiang, Yuan (Author) / Zhou, Zhi-Hua (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / Ira A. Fulton Schools of Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2012-05-23