  In the recording studio, producers of Electronic Dance Music (EDM) spend more
time creating, shaping, mixing and mastering sounds, than with compositional
aspects or arrangement. They tune the sound by close listening and by
leveraging audio metering and audio analysis tools, until they successfully
creat the desired sound aesthetics. DJs of EDM tend to play sets of songs that
meet their sound ideal. We therefore suggest using audio metering and
monitoring tools from the recording studio to analyze EDM, instead of relying
on conventional low-level audio features. We test our novel set of features by
a simple classification task. We attribute songs to DJs who would play the
specific song. This new set of features and the focus on DJ sets is targeted at
EDM as it takes the producer and DJ culture into account. With simple
dimensionality reduction and machine learning these features enable us to
attribute a song to a DJ with an accuracy of 63%. The features from the audio
metering and monitoring tools in the recording studio could serve for many
applications in Music Information Retrieval, such as genre, style and era
classification and music recommendation for both DJs and consumers of
electronic dance music.
