Novel Recording Studio Features for Music Information Retrieval

TL;DR

This paper introduces novel audio features derived from recording studio tools, aimed at improving music information retrieval tasks in EDM by analyzing sound aesthetics and DJ preferences.

Contribution

It proposes a new set of features based on studio audio metering tools and demonstrates their effectiveness in attributing songs to DJs with 63% accuracy.

Findings

Features enable DJ attribution with 63% accuracy.

Studio-based features can improve genre and style classification.

Potential applications include music recommendation and classification.

Abstract

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.