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B. De Man, and JO. D.. Reiss, "A Knowledge-Engineered Autonomous Mixing System," Paper 8961, (2013 October.). doi: B. De Man, and JO. D.. Reiss, "A Knowledge-Engineered Autonomous Mixing System," Paper 8961, (2013 October.). doi:
Abstract: 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.

@article{de man2013a,
author={de man, brecht and reiss, joshua d.},
journal={journal of the audio engineering society},
title={a knowledge-engineered autonomous mixing system},
year={2013},
volume={},
number={},
pages={},
doi={},
month={october},} @article{de man2013a,
author={de man, brecht and reiss, joshua d.},
journal={journal of the audio engineering society},
title={a knowledge-engineered autonomous mixing system},
year={2013},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={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.},}

TY - paper
TI - A Knowledge-Engineered Autonomous Mixing System
SP - EP -
AU - De Man, Brecht
AU - Reiss, Joshua D.
PY - 2013
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2013 TY - paper
TI - A Knowledge-Engineered Autonomous Mixing System
SP - EP -
AU - De Man, Brecht
AU - Reiss, Joshua D.
PY - 2013
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2013
AB - 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.