The number of parameters of modern audio effects easily ranges in the dozens. Expert knowledge is required to understand which parameter change results in a desired effect. Yet, such sound processors are also making their way into consumer products, where they tend to overburden most users. Hence, we propose a procedure to achieve a desired effect without technical expertise based on a black-box genetic optimization strategy: Users are only con-fronted with a series of comparisons of two differently processed sound examples. Learning from the users’ choices, our software optimizes the parameter settings. We conducted a study on hearing-impaired persons without expert knowledge who used the system to adjust a third-octave equalizer and a multiband compressor to improve the intelligibility of a TV set.
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