Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Generating remote control interfaces for complex appliances
Proceedings of the 15th annual ACM symposium on User interface software and technology
SUPPLE: automatically generating user interfaces
Proceedings of the 9th international conference on Intelligent user interfaces
subjEQt: controlling an equalizer through subjective terms
CHI '06 Extended Abstracts on Human Factors in Computing Systems
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
CueFlik: interactive concept learning in image search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
MySong: automatic accompaniment generation for vocal melodies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Dynamic mapping of physical controls for tabletop groupware
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CHI '09 Extended Abstracts on Human Factors in Computing Systems
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
Bucking the trend: large-scale cost-focused active learning for statistical machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Proceedings of the 20th ACM international conference on Multimedia
Physical modelling and supervised training of a virtual string quartet
Proceedings of the 21st ACM international conference on Multimedia
Active learning of intuitive control knobs for synthesizers using gaussian processes
Proceedings of the 19th international conference on Intelligent User Interfaces
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Potential users of audio production software, such as audio equalizers, may be discouraged by the complexity of the interface and a lack of clear affordances in typical interfaces. In this work, we create a personalized on-screen slider that lets the user manipulate the audio with an equalizer in terms of a descriptive term (e.g. "warm"). The system learns mappings by presenting a sequence of sounds to the user and correlating the gain in each frequency band with the user's preference rating. This method is extended and improved on by incorporating knowledge from a database of prior concepts taught to the system by prior users. This is done with a combination of active learning and simple transfer learning. Results on a study of 35 participants show personalized audio manipulation tool can be built with 10 times fewer interactions than is possible with the baseline approach.