Information-based objective functions for active data selection
Neural Computation
Neural network exploration using optimal experiment design
Neural Networks
Gaussian Process Regression: Active Data Selection and Test Point Rejection
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
subjEQt: controlling an equalizer through subjective terms
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
CueFlik: interactive concept learning in image search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exposing parameters of a trained dynamic model for interactive music creation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A method for rapid personalization of audio equalization parameters
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Description-based design of melodies
Computer Music Journal
Active Data Selection for Sensor Networks with Faults and Changepoints
AINA '10 Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
A Bayesian interactive optimization approach to procedural animation design
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Building a personalized audio equalizer interface with transfer learning and active learning
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
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Typical synthesizers only provide controls to the low-level parameters of sound-synthesis, such as wave-shapes or filter envelopes. In contrast, composers often want to adjust and express higher-level qualities, such as how "scary" or "steady" sounds are perceived to be. We develop a system which allows users to directly control abstract, high-level qualities of sounds. To do this, our system learns functions that map from synthesizer control settings to perceived levels of high-level qualities. Given these functions, our system can generate high-level knobs that directly adjust sounds to have more or less of those qualities. We model the functions mapping from control-parameters to the degree of each high-level quality using Gaussian processes, a nonparametric Bayesian model. These models can adjust to the complexity of the function being learned, account for nonlinear interaction between control-parameters, and allow us to characterize the uncertainty about the functions being learned. By tracking uncertainty about the functions being learned, we can use active learning to quickly calibrate the tool, by querying the user about the sounds the system expects to most improve its performance. We show through simulations that this model-based active learning approach learns high-level knobs on certain classes of target concepts faster than several baselines, and give examples of the resulting automatically- constructed knobs which adjust levels of non-linear, high- level concepts.