Resources Electronic resources mentioned in this thesis are available here. This system, called the Wekinator, supports human interaction throughout the entire supervised learning process, including the generation of training examples and the application of Rebecca fiebrink thesis models to real-time inputs.
This thesis also presents work utilizing the Wekinator to study human-computer interaction with supervised learning in computer music. The Wekinator is published as a freely-available, open source software project, and several composers have already employed it in the creation of new musical instruments and compositions.
This work endeavors to gain a deeper knowledge of the human factors surrounding the application of supervised learning to these types of problems, to make supervised learning algorithms more usable by musicians, and to study how supervised learning can function as a creative tool. Can we better understand the real-world consequences of algorithm choices and user interface designs for end-user machine learning?
Research is presented which includes a participatory design process with practicing composers, pedagogical use with non-expert users in an undergraduate classroom, a study of the design of a gesture recognition system for a sensor-augmented cello bow, and case studies with three composers who have used the system in completed artistic works.
Though varied, many of these problems may be addressed using machine learning techniques, including supervised learning in particular.
The scope of the research presented here is the application of supervised learning algorithms to contemporary computer music composition and performance. Can we make machine learning algorithms more usable and useful?
Computer music is a domain rich with computational problems requiring the modeling of complex phenomena, the construction of real-time interactive systems, and the support of human creativity. This work both empowers musicians to create new forms of art and contributes to a broader HCI perspective on machine learning practice.
This thesis presents a general-purpose software system for applying standard supervised learning algorithms in music and other real-time problem domains.
How can human interaction play a role in enabling users to efficiently create useful machine learning systems, in enabling successful application of algorithms by machine learning novices, and in ultimately making it possible in practice to apply machine learning to new problems?Rebecca Fiebrink Curriculum Vitae Senior Lecturer, Department of Computing Goldsmiths, University of London [email protected] recognition system for music performance as my master thesis.
Finally, I would like to extend my appreciation to my parents, Dejiong Tang and Zhuli Zhen for raising me and support me in the past twenty-five years.
I would like. This thesis presents a general-purpose software system for applying standard supervised learning algorithms in music and other real-time problem domains. This system, called the Wekinator, supports human interaction throughout the entire supervised learning process, including the generation of training examples and the application of trained models to.
N-GRAM MODELING OF TABLA SEQUENCES USING VARIABLE-LENGTH HIDDEN MARKOV MODELS FOR IMPROVISATION AND COMPOSITION A Thesis Presented to The Academic Faculty by Avinash Sastry Dr. Rebecca Fiebrink Department of Computer Science Princeton University.
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