Real-time Human Interaction with Supervised Learning Algorithms for Music Composition and Performance

This thesis examines machine learning through the lens of human-computer interaction in order to address fundamental questions surrounding the application of machine learning to real-life problems, including: Can we make machine learning algorithms more usable? Can we better understand the real-world consequences of algorithm choices and user interface designs? How can we devise more effective human workflows for building machine learning systems, enable more successful application of algorithms by machine learning novices, and ultimately make it possible in practice to apply machine learning to new problems? The scope of the research presented here is the application of supervised learning algorithms to contemporary computer music composition and performance. 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. Though varied, many of these problems may be addressed using machine learning techniques, including supervised learning in particular. 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. 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 real-time inputs. 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.