The field of audio engineering has historically been male dominated, and has thus led to a male-biased mindset within the industry. Despite evidence of an increasing number of women involved with audio engineering and high-quality home audio systems [1], this mindset has not changed drastically; similar beliefs are still displayed about the role of women [2] throughout industry and academia - with anecdotal evidence of this mindset cropping up at professional conferences.
Professional associations are a useful indicator for analysing the intersection between industry and academia. One such association within the field of audio engineering is the Audio Engineering Society (AES). The AES, formed in 1948, has more than 12,000 members worldwide, and regularly organises conferences, conventions and the publication of a monthly journal. Recently, the AES has started to make attempts to combat the issues of gender diversity within audio engineering through the formation of a Diversity and Inclusion Committee, and the alignment of the British section of the AES with the UNWomen ‘HeForShe’ campaign.
However, the first stage in being able to address any problems is to fully characterise it. The figures below shows the distribution of author gender across AES conferences from 2012-2019, according to a number of variables of interest. An associated IEEE article describes the methodology used and findings in detail (doi.org/10.1109/TE.2018.2814613). The associated dataset (2012-2016) is available at: (doi.org/10.5281/zenodo.1249693).
This on-going study is part of the Gender Diversity in Audio project at the AudioLab, University of York.
A list of authors was generated for AES conferences from 2012-2019 (inclusive) using the conference proceedings available online. For each author in the generated list, gender is determined via a multi-step process where self identified pronoun of the author takes priority. If this step doesn’t produce a result, several subsequent steps are employed. If no gender can be determined, the entry is labelled as ‘Unknown’.
Select the dataset range of interest. 2012-2016 aligns with the data discussed in the IEEE paper. Hover over the bars to see the numerical breakdown for each result. Click on the legend labels (Non-binary, Unknown, Female, Male) to re-order the results by that label.
Please use a modern browser with JavaScript enabled to see the interactive results.
Lauren Tomasello
Gavin Kearney
Amelia Gully
Rebecca Vos