a perceptual assistant to do sound equalization
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.
intelligent interfaces learning expert systems audio equalization perceptual tools