Matching Items (41)
Filtering by
ContributorsJin, Leon (Performer) / Duo, Hongzuo (Performer) / Bergstedt, David (Performer) / Ellis, Gage (Performer) / Novak, Gail (Performer) / ASU Library. Music Library (Publisher)
Created2021-02-24
ContributorsWaters, Jared (Performer) / Creviston, Hannah (Performer) / Liu, Miao (Performer) / Guo, Hongzuo (Performer) / DeLaCruz, Nathaniel (Performer) / LoGuidice, Rosa (Performer) / Chiko, Ty (Performer) / Gatchel, David (Performer) / ASU Library. Music Library (Publisher)
Created2021-01-28
ContributorsKosminov, Vladislav (Performer) / Solari, John (Performer) / Liu, Shiyu (Performer) / Huang, Anruo (Performer) / Holly, Sean (Performer) / Novak, Gail (Performer) / Yang, Elliot (Performer) / Wu, Selene (Performer) / Kinnard, Zachariah (Performer) / Kuebelbeck, Stephen (Performer) / Johnson, Kaitlyn (Performer) / Bosworth, Robert (Performer) / Matejek, Ryan (Performer) / ASU Library. Music Library (Publisher)
Created2021-01-27
Description
In order to cope with the decreasing availability of symphony jobs and collegiate faculty positions, many musicians are starting to pursue less traditional career paths. Also, to combat declining audiences, musicians are exploring ways to cultivate new and enthusiastic listeners through relevant and engaging performances. Due to these challenges, many community-based chamber music ensembles have been formed throughout the United States. These groups not only focus on performing classical music, but serve the needs of their communities as well. The problem, however, is that many musicians have not learned the business skills necessary to create these career opportunities. In this document I discuss the steps ensembles must take to develop sustainable careers. I first analyze how groups build a strong foundation through getting to know their communities and creating core values. I then discuss branding and marketing so ensembles can develop a public image and learn how to publicize themselves. This is followed by an investigation of how ensembles make and organize their money. I then examine the ways groups ensure long-lasting relationships with their communities and within the ensemble. I end by presenting three case studies of professional ensembles to show how groups create and maintain successful careers. Ensembles must develop entrepreneurship skills in addition to cultivating their artistry. These business concepts are crucial to the longevity of chamber groups. Through interviews of successful ensemble members and my own personal experiences in the Tetra String Quartet, I provide a guide for musicians to use when creating a community-based ensemble.
ContributorsDalbey, Jenna (Author) / Landschoot, Thomas (Thesis advisor) / McLin, Katherine (Committee member) / Ryan, Russell (Committee member) / Solis, Theodore (Committee member) / Spring, Robert (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The results suggested that, depending on the nature of data, optimal specification of (1) decision rules to select the covariate and its split value in a Classification Tree, (2) the number of covariates randomly sampled for selection, and (3) methods of estimating Random Forests propensity scores could potentially produce an unbiased average treatment effect estimate after propensity scores weighting by the odds adjustment. Compared to the logistic regression estimation model using the true propensity score model, Random Forests had an additional advantage in producing unbiased estimated standard error and correct statistical inference of the average treatment effect. The relationship between the balance on the covariates' means and the bias of average treatment effect estimate was examined both within and between conditions of the simulation. Within conditions, across repeated samples there was no noticeable correlation between the covariates' mean differences and the magnitude of bias of average treatment effect estimate for the covariates that were imbalanced before adjustment. Between conditions, small mean differences of covariates after propensity score adjustment were not sensitive enough to identify the optimal Random Forests model specification for propensity score analysis.
ContributorsCham, Hei Ning (Author) / Tein, Jenn-Yun (Thesis advisor) / Enders, Stephen G (Thesis advisor) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2013
ContributorsEslava, Martin Hans (Performer) / LoGuidice, Rosa (Performer) / Landau, Tanya (Performer) / Nguyen, Sarahkim (Performer) / ASU Library. Music Library (Publisher)
Created2021-03-19
ContributorsLupe, Samuel (Performer) / Novak, Gail (Performer) / Leyba, Jakob (Performer) / Wu, Shengwen (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-22
Description
The act of writing music criticism comes with numerous difficulties inherently involved in describing the ineffable qualities of audio in the medium of the written word. My music criticism podcast, Here and Now with Steve Jozef, offers a method of reviewing albums that sidesteps these problems by interweaving the criticism with clips of the songs discussed. This format holds the critic accountable to root their opinion in tangible components in the music, and makes clear the direct line between the qualities of the music at hand and the reviewer's verdict on that music. I wrote, recorded, edited, and distributed 13 episodes of the podcast over the course of six months; I've selected four of those episodes to represent my work for the purposes of the thesis. Their scripts, as well as links to the full episodes, are included with this paper. In addition, I thoroughly researched modern academic thought on the nature, purpose, and future of popular music criticism. This research helped me refine a conception of my ideal theoretical audience for the podcast, forced me to confront and assess my music evaluation criteria through various lenses, and develop the goal of creating a pattern of empathetic listening amongst my listeners. Over the course of developing the project, I ran into several obstacles in using copyrighted material on the show without permission from the copyright holders. Thus the podcast also became a case study in evaluating modern copyright law and its application, and demonstrating what needs to change about both for music criticism to sustain in this form.
ContributorsJozef, Steven James (Author) / Wells, Christopher (Thesis director) / Duerden, Sarah (Committee member) / Tinapple, David (Committee member) / Department of English (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05