Learning to control a reverberator using subjective perceptual descriptors

The complexity of existing tools for mastering audio can be daunting. Moreover, many people think about sound in individualistic terms (such as "boomy") that may not have clear mappings onto the controls of existing audio tools. We propose learning to map subjective audio descriptors, such as "boomy", onto measures of signal properties in order to build a simple controller that manipulates an audio reverberator in terms of a chosen descriptor. For example, "make the sound less boomy". In the learning process, a user is presented with a series of sounds altered in different ways by a reverberator and asked to rate how well each sound represents the audio concept. The system correlates these ratings with reverberator parameters to build a controller that manipulates reverberation in the user's terms. In this paper, we focus on developing the mapping between reverberator controls, measures of qualities of reverberation and user ratings. Results on 22 subjects show the system learns quickly (under 3 minutes of training per concept), predicts users responses well (mean correlation coefficient of system predictiveness 0.75) and meets users' expectations (average human rating of 7.4 out of 10).