Still at the upcoming International Conference on Digital Audio Effects in Edinburgh, 5-8 September, our group’s Brecht De Man will be presenting a paper on his Mix Evaluation Dataset (a pre-release of which can be read here).
It is a collection of mixes and evaluations of these mixes, amassed over the course of his PhD research, that has already been the subject of several studies on best practices and perception of mix engineering processes.
With over 180 mixes of 18 different songs, and evaluations from 150 subjects totalling close to 13k statements (like ‘snare drum too dry’ and ‘good vocal presence’), the dataset is certainly the largest and most diverse of its kind.
Unlike the bulk of previous research in this topic, the data collection methodology presented here has maximally preserved ecological validity by allowing participating mix engineers to use representative, professional tools in their preferred environment. Mild constraints on software, such as the agreement to use the DAW’s native plug-ins, means that mixes can be recreated completely and analysed in depth from the DAW session files, which are also shared.
The listening test experiments offered a unique opportunity for the participating mix engineers to receive anonymous feedback from peers, and helped create a large body of ratings and free-field text comments. Annotation and analysis of these comments further helped understand the relative importance of various music production aspects, as well as correlate perceptual constructs (such as reverberation amount) with objective features.
An interface to browse the songs, audition the mixes, and dissect the comments is provided at http://c4dm.eecs.qmul.ac.uk/multitrack/MixEvaluation/, from where the audio (insofar the source is licensed under Creative Commons, or copyrighted but available online) and perceptual evaluation data can be downloaded as well.