a method for rapid personalization of audio equalization parameters
Potential users of audio production software, such as audio equalizers, may be discouraged by the complexity of the interface. We describe a system that simplifies the interface by quickly mapping an individual's preferred sound manipulation onto parameters for audio equalization. This system learns mappings by presenting a sequence of equalizer settings to the user and correlating the gain in each frequency band with the user's preference rating. Learning typically converges in 25 user ratings (under two minutes). The system then creates a simple on-screen slider that lets the user manipulate the audio in terms of the descriptive term, without need to learn or use the parameters of an equalizer. Results are reported on the speed and effectiveness of the system for a set of 19 users and a set of five descriptive terms.
audio equalization interface peronalization music