Communications of the ACM - Special issue on parallelism
IEEE Spectrum
Aaron's code
Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Survey of current speech technology
Communications of the ACM
The further exploits of Aaron, painter
Stanford Humanities Review
Machine Learning
Automatic noise gate settings for drum recordings containing bleed from secondary sources
EURASIP Journal on Advances in Signal Processing - Special issue on digital audio effects
MIXPLORATION: rethinking the audio mixer interface
Proceedings of the 19th international conference on Intelligent User Interfaces
| Hi-index | 0.00 |
This paper describes an intelligent interface to assist in the expert perceptual task of sound equalization. This is commonly done by a sound engineer in a recording studio, live concert setting, or in setting up audio systems. The system uses inductive learning to acquire expert skill using nearest neighbor pattern recognition. This skill is then used in a sound equalization expert system, which learns to proficiently adjust the timbres (tonal qualities) of brightness, darkness, and smoothness in a context-dependent fashion. The computer is used as a tool to sense, process, and act in helping the user perform a perceptual task. Adjusting timbres of sound is complicated by the fact that there are non-linear relationships between equalization adjustments and perceived sound quality changes. The developed system shows that the nearest-neighbor context-dependent equalization is rated 68% higher than the set linear average equalization and that it is preferred 81% of the time.