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G. Wichern, A. Wishnick, A. Lukin, and H. Robertson, "Comparison of Loudness Features for Automatic Level Adjustment in Mixing," Paper 9370, (2015 October.). doi: G. Wichern, A. Wishnick, A. Lukin, and H. Robertson, "Comparison of Loudness Features for Automatic Level Adjustment in Mixing," Paper 9370, (2015 October.). doi:
Abstract: Manually setting the level of each track of a multitrack recording is often the first step in the mixing process. In order to automate this process, loudness features are computed for each track and gains are algorithmically adjusted to achieve target loudness values. In this paper we first examine human mixes from a multitrack dataset to determine instrument-dependent target loudness templates. We then use these templates to develop three different automatic level-based mixing algorithms. The first is based on a simple energy-based loudness model, the second uses a more sophisticated psychoacoustic model, and the third incorporates masking effects into the psychoacoustic model. The three automatic mixing approaches are compared to human mixes using a subjective listening test. Results show that subjects preferred the automatic mixes created from the simple energy-based model, indicating that the complex psychoacoustic model may not be necessary in an automated level setting application.

@article{wichern2015comparison,
author={wichern, gordon and wishnick, aaron and lukin, alexey and robertson, hannah},
journal={journal of the audio engineering society},
title={comparison of loudness features for automatic level adjustment in mixing},
year={2015},
volume={},
number={},
pages={},
doi={},
month={october},} @article{wichern2015comparison,
author={wichern, gordon and wishnick, aaron and lukin, alexey and robertson, hannah},
journal={journal of the audio engineering society},
title={comparison of loudness features for automatic level adjustment in mixing},
year={2015},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={manually setting the level of each track of a multitrack recording is often the first step in the mixing process. in order to automate this process, loudness features are computed for each track and gains are algorithmically adjusted to achieve target loudness values. in this paper we first examine human mixes from a multitrack dataset to determine instrument-dependent target loudness templates. we then use these templates to develop three different automatic level-based mixing algorithms. the first is based on a simple energy-based loudness model, the second uses a more sophisticated psychoacoustic model, and the third incorporates masking effects into the psychoacoustic model. the three automatic mixing approaches are compared to human mixes using a subjective listening test. results show that subjects preferred the automatic mixes created from the simple energy-based model, indicating that the complex psychoacoustic model may not be necessary in an automated level setting application.},}

TY - paper
TI - Comparison of Loudness Features for Automatic Level Adjustment in Mixing
SP - EP -
AU - Wichern, Gordon
AU - Wishnick, Aaron
AU - Lukin, Alexey
AU - Robertson, Hannah
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2015 TY - paper
TI - Comparison of Loudness Features for Automatic Level Adjustment in Mixing
SP - EP -
AU - Wichern, Gordon
AU - Wishnick, Aaron
AU - Lukin, Alexey
AU - Robertson, Hannah
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2015
AB - Manually setting the level of each track of a multitrack recording is often the first step in the mixing process. In order to automate this process, loudness features are computed for each track and gains are algorithmically adjusted to achieve target loudness values. In this paper we first examine human mixes from a multitrack dataset to determine instrument-dependent target loudness templates. We then use these templates to develop three different automatic level-based mixing algorithms. The first is based on a simple energy-based loudness model, the second uses a more sophisticated psychoacoustic model, and the third incorporates masking effects into the psychoacoustic model. The three automatic mixing approaches are compared to human mixes using a subjective listening test. Results show that subjects preferred the automatic mixes created from the simple energy-based model, indicating that the complex psychoacoustic model may not be necessary in an automated level setting application.