Music recommendation based on acoustic features from the recording studio

Abstract

Producers of Electronic Dance Music (EDM) typically spend more time creating, shaping, mixing and mastering sounds, than with aspects of composition and arrangement. They analyze the sound by close listening and by leveraging audio monitoring tools, until they successfully created the desired sound character. DJs of EDM tend to play sets of songs that meet their sound ideal. We use audio monitoring tools from the recording studio to retrieve the sound ideal of the most popular DJs and perform a DJ classification, e.g., to predict “what your favorite DJ would play.” The features include third-octave band VU, RMS and crest factor meters, phase scope, and the channel correlation coefficient. 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 recording studio features enable us to attribute a song to a DJ with an accuracy of 63%. The features from the monitoring tools in the recording studio could serve for many applications in music information retrieval, such as genre, style and era classification for music browsing, automatic playlist generation and music recommendation, especially in electronic dance music.