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Schedule as of Oct 11, 2022 - subject to change

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Thursday, October 27 • 3:15pm - 3:45pm
Measuring Audibility Threshold Levels for Attack and Release in a Dynamic Range Compressor

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Dynamic range compressors are one of the most ubiquitous audio effects used in music production and mixing contexts. These effects manifest themselves in both analog and digital production workflows. The ballistic settings, of attack time and release time, of a dynamic range compressor (DRC) are considered highly important for influencing the perceived response of the compressor, and the manner in which they shape an audio signal. The timing controls have also been shown to greatly influence the perception of the sonic attributes and style of the program material. The ability to perceive the effect of compression ballistics is important to better understand how these tools can be best utilised in music production. We present an audibility threshold test, in a web-based format, using an ABX test methodology, where participants were asked to identify which of the presented audio examples they perceived as different to a reference file. DRC attack and release settings were varied and applied to a set of audio examples. The results demonstrate that large changes in the ballistics settings, particularly in the case of the release time, are more noticeable than small changes, and that individuals were unable to perceive a release change of less than 50ms. The test also demonstrated that the perception of changes in ballistics settings vary when compressor threshold settings were changed. Lighter levels of compression with a higher threshold generally yielded more noticeable results, especially where the effects of large changes in ballistics settings were measured. Finally, it was observed that participants with above average or expert levels of experience (4 years or more) were more likely to perceive small differences in attack and release times than those with less developed critical listening skills.

Speakers
avatar for Gary Bromham

Gary Bromham

Researcher & Independent Music Professional, Queen Mary University of London
DM

David Moffat

AI&ML Data Scientist, Plymouth Marine Laboratory
Dr. David Moffat is an AI and ML researcher at Plymouth Marine Laboratory. He has previously worked in audio signal processing, intelligent music production, audio analysis, psychoacoustics, perception, sound syntheisis audio fro games and ML applied to a range of different signal... Read More →


Thursday October 27, 2022 3:15pm - 3:45pm EDT
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