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D. Moffat, B. De Man, and JO. D.. Reiss, "Semantic Music Production: A Meta-Study," J. Audio Eng. Soc., vol. 70, no. 7/8, pp. 548-564, (2022 July.). doi: D. Moffat, B. De Man, and JO. D.. Reiss, "Semantic Music Production: A Meta-Study," J. Audio Eng. Soc., vol. 70 Issue 7/8 pp. 548-564, (2022 July.). doi:
Abstract: This paper presents a systematic review of semantic music production, including a meta-analysis of three studies into how individuals use words to describe audio effects within music production. Each study followed different methodologies and stimuli. The SAFE project created audio effect plug-ins that allowed users to report suitable words to describe the perceived result. SocialFX crowdsourced a large data set of how non-professionals described the change that resulted from an effect applied to an audio sample. The Mix Evaluation Data Set performed a series of controlled studies in which students used natural language to comment extensively on the content of different mixes of the same groups of songs. The data sets provided 40,411 audio examples and 7,221 unique word descriptors from 1,646 participants. Analysis showed strong correlations between various audio features, effect parameter settings, and semantic descriptors. Meta-analysis not only revealed consistent use of descriptors among the data sets but also showed key differences that likely resulted from the different participant groups and tasks. To the authors' knowledge, this represents the first meta-study and the largest-ever analysis of music production semantics.

@article{moffat2022semantic,
author={moffat, david and de man, brecht and reiss, joshua d.},
journal={journal of the audio engineering society},
title={semantic music production: a meta-study},
year={2022},
volume={70},
number={7/8},
pages={548-564},
doi={},
month={july},} @article{moffat2022semantic,
author={moffat, david and de man, brecht and reiss, joshua d.},
journal={journal of the audio engineering society},
title={semantic music production: a meta-study},
year={2022},
volume={70},
number={7/8},
pages={548-564},
doi={},
month={july},
abstract={this paper presents a systematic review of semantic music production, including a meta-analysis of three studies into how individuals use words to describe audio effects within music production. each study followed different methodologies and stimuli. the safe project created audio effect plug-ins that allowed users to report suitable words to describe the perceived result. socialfx crowdsourced a large data set of how non-professionals described the change that resulted from an effect applied to an audio sample. the mix evaluation data set performed a series of controlled studies in which students used natural language to comment extensively on the content of different mixes of the same groups of songs. the data sets provided 40,411 audio examples and 7,221 unique word descriptors from 1,646 participants. analysis showed strong correlations between various audio features, effect parameter settings, and semantic descriptors. meta-analysis not only revealed consistent use of descriptors among the data sets but also showed key differences that likely resulted from the different participant groups and tasks. to the authors' knowledge, this represents the first meta-study and the largest-ever analysis of music production semantics.},}

TY - reviewPaper
TI - Semantic Music Production: A Meta-Study
SP - 548 EP - 564
AU - Moffat, David
AU - De Man, Brecht
AU - Reiss, Joshua D.
PY - 2022
JO - Journal of the Audio Engineering Society
IS - 7/8
VO - 70
VL - 70
Y1 - July 2022 TY - reviewPaper
TI - Semantic Music Production: A Meta-Study
SP - 548 EP - 564
AU - Moffat, David
AU - De Man, Brecht
AU - Reiss, Joshua D.
PY - 2022
JO - Journal of the Audio Engineering Society
IS - 7/8
VO - 70
VL - 70
Y1 - July 2022
AB - This paper presents a systematic review of semantic music production, including a meta-analysis of three studies into how individuals use words to describe audio effects within music production. Each study followed different methodologies and stimuli. The SAFE project created audio effect plug-ins that allowed users to report suitable words to describe the perceived result. SocialFX crowdsourced a large data set of how non-professionals described the change that resulted from an effect applied to an audio sample. The Mix Evaluation Data Set performed a series of controlled studies in which students used natural language to comment extensively on the content of different mixes of the same groups of songs. The data sets provided 40,411 audio examples and 7,221 unique word descriptors from 1,646 participants. Analysis showed strong correlations between various audio features, effect parameter settings, and semantic descriptors. Meta-analysis not only revealed consistent use of descriptors among the data sets but also showed key differences that likely resulted from the different participant groups and tasks. To the authors' knowledge, this represents the first meta-study and the largest-ever analysis of music production semantics.