Automatic Detection of Cue Points for the Emulation of DJ Mixing

Abstract

The automatic identification of cue points is a central task in applications as diverse as music thumbnailing, generation of mash ups, and DJ mixing. Our focus lies in electronic dance music and in a specific kind of cue point, the “switch point,” that makes it possible to automatically construct transitions between tracks, mimicking what professional DJs do. We present two approaches for the detection of switch points. One embodies a few general rules we established from interviews with professional DJs, the other models a manually annotated dataset that we curated. Both approaches are based on feature extraction and novelty analysis. From an evaluation conducted on previously unknown tracks, we found that about 90% of the points generated can be reliably used in the context of a DJ mix.