In this paper a knowledge-engineered mixing engine is introduced that uses semantic mixing rules and bases mixing decisions on instrument tags as well as elementary, low-level signal features. Mixing rules are derived from practical mixing engineering textbooks. The performance of the system is compared to existing automatic mixing tools as well as human engineers by means of a listening test, and future directions are established.
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