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Automatic Masking Reduction in Balance Mixes Using Evolutionary Computing

Music production is a highly subjective task, which can be difficult to automate. Simple session structures can quickly expose complex mathematical tasks which are difficult to optimize. This paper presents a method for the reduction of masking in an unknown mix using genetic programming. The model uses results from a series of listening tests to guide its cost function. The program then returns a vector that best minimizes this cost. The paper explains the limitations of using such a method for audio as well as validating the results.Music production is a highly subjective task, which can be difficult to automate. Simple session structures can quickly expose complex mathematical tasks which are difficult to optimize. This paper presents a method for the reduction of masking in an unknown mix using genetic programming. The model uses results from a series of listening tests to guide its cost function. The program then returns a vector that best minimizes this cost. The paper explains the limitations of using such a method for audio as well as validating the results.

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Permalink: http://www.aes.org/e-lib/browse.cfm?elib=19210

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