Rebecca Fiebrink

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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(More)
Model evaluation plays a special role in interactive machine learning (IML) systems in which users rely on their assessment of a model's performance in order to determine how to improve it. A better understanding of what model criteria are important to users can therefore inform the design of user interfaces for model evaluation as well as the choice and(More)
This paper presents ACE (Autonomous Classification Engine), a framework for using and optimizing classifiers. Given a set of feature vectors, ACE experiments with a variety of classifiers, classifier parameters, classifier ensembles and dimensionality reduction techniques in order to arrive at a good configuration for the problem at hand. In addition to(More)
Supervised learning methods have long been used to allow musical interface designers to generate new mappings by example. We propose a method for harnessing machine learning algorithms within a radically interactive paradigm, in which the designer may repeatedly generate examples, train a learner, evaluate outcomes, and modify parameters in real-time within(More)
We draw on our experiences with the Princeton Laptop Orchestra to discuss novel uses of the laptop's native physical inputs for flexible and expressive control. We argue that instruments designed using these built-in inputs offer benefits over custom standalone controllers, particularly in certain group performance settings; creatively thinking about native(More)
Hierarchical taxonomies of classes arise in the analysis of many types of musical information, including genre, as a means of organizing overlapping categories at varying levels of generality. However, incorporating hierarchical structure into conventional machine learning systems presents a challenge: the use of independent binary classifiers for each(More)
In this paper, we discuss our recent additions of audio analysis and machine learning infrastructure to the ChucK music programming language, wherein we provide a complementary system prototyping framework for MIR researchers and lower the barriers to applying many MIR algorithms in live music performance. The new language capabilities preserve ChucK’s(More)
Multi-touch interactions are a promising means of control for interactive tabletops. However, a lack of precision and tactile feedback makes multi-touch controls a poor fit for tasks where precision and feedback are crucial. We present an approach that offers precise control and tactile feedback for tabletop systems through the integration of dynamically(More)
While several researchers have grappled with the problem of comparing musical devices across performance, installation, and related contexts, no methodology yet exists for producing holistic, informative visualizations for these devices. Drawing on existing research in performance interaction, human-computer interaction, and design space analysis, the(More)