Izotope ozone10/29/2023 ![]() Machine learning makes doing something like genre detection much easier than it would be with a traditional algorithmic approach. Other systems use AI to set targets, and in fact, this is one of the common uses of AI in mastering. To some extent, this assumes that you know what you want and need out of mastering, however, it doesn’t give you that much control over what’s going on in the background. Some automated mastering systems, like Aria or CloudBounce, require input from the end user to select these targets. Selecting appropriate targets is where it gets tricky. Generally, automated mastering attempts to apply processing that will match the measured values to preselected targets. It’s easy enough to measure the integrated loudness or extract a spectral analysis to determine overall tonal balance, but what you do with that information makes a big difference to the outcome. The success of an automated mastering algorithm depends heavily on the thought put into it by the developer. Thus, while AI mastering is a type of automated mastering, not all automated mastering is necessarily AI mastering. Rather, it can be guided by the human intelligence of the designer who writes the code. However, none of this necessarily requires artificial intelligence. This could include things like setting final levels, setting EQ to improve tonal balance, and even setting parameters for things like compression or saturation. Automated mastering attempts to use computer algorithms to quickly and automatically complete some of the common steps frequently employed in audio mastering.
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