Understanding how a human mixing engineer functions is necessary for the design of intelligent music production tools. The goal of such tools is to generate mixes that could realistically have been created by a human mix-engineer. This paper presents an analysis of 1501 mixes, over 10 different songs, created by mix-engineers. The number of mixes of each song ranged from 97 to 373. A variety of objective signal features were extracted and principal component analysis was performed revealing four dimensions of mix-variation, which can be described as amplitude, brightness, bass, and width. Feature distribution suggests multimodal behavior dominated by one specific mode. This distribution appears to be independent of the choice of song, but with variation in modal parameters. This is then used to obtain general trends and tolerance bounds for these features. The results presented here are useful as parametric guidance for intelligent music production systems. This provides insight into the creative decision making processes of mix-engineers.
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