Learn More
This paper presents ACE (Autonomous Classification Engine), a framework for using and optimizing classifi-ers. 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)
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)
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)
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)
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)
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)
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)