The automatic mixer research distinguishes the engineering mixing from the subjective mixing. Therefore the current research is focused on a constrained rule mixing layer and a subjective mixing layer. The rule-based section is based on engineering constraints while the subjective layer is based on a target mixing style. This target style can be extracted from previously mixed songs based on feature extraction. Two approaches are under study. One is a modified automatic mixer, whose settings can be adapted based on target features and the other is based on a multilayer feedback network. The target mixing methods rely on output feature similarity to the reference features of the target mix. It is the current belief of the author that the use of expert training data can be used to increase the convergence rate of the system.

Currently automated mixers are capable of saving a timeline of static mix scenes, which can be loaded for later use. But they lack the ability to adapt to a different room or to a different set of inputs. In other words, they lack the ability to automatically taking mixing decisions. In the current research approach the starting point is a target mixing style, rather than a fixed prerecorded setup. This has the advantage of being able to blend a mixing style of a completely different song into an unknown set of inputs.

The justification of this research is the need of non-expert audio operators and musicians to be able to achieve a quality mix with minimal effort. Currently mixing is a task which requires grate skills, practice and can be sometime tedious. For the professional mixing engineer this kind of tool will reduce sound check time and will prove useful in multiple music group and festivals where changing from one group to another should be done really quickly. Currently large audio productions tend to have hundreds of channels, being able to group some of those channels into an automatic mode will ease the mixing task to the audio engineer. There is also the possibility of applying this technology to remote mixing applications where latency is too large to be able to interact with all aspects of the mix

This research is pursuing the knowledge required to develop automatic mixtures comparable in quality to those performed by professional human mixing console operators. Implementation, subjective comparison and error distance measure between a target mixture style and the automatic mixture will measure the success of the results. By style we refer not only to a certain genre of music but also to a producer or engineer subjective contribution to a mix.

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