Automatic Detection of Cue Points for DJ Mixing

TL;DR

This paper introduces an automated method for detecting cue points, specifically switch points, in electronic dance music to facilitate DJ mixing, using features extraction and novelty analysis based on professional DJ insights.

Contribution

It presents a novel approach for automatic switch point detection in EDM, combining DJ interview insights with feature extraction and novelty analysis techniques.

Findings

96% of generated switch points are suitable for DJ mixing

Method outperforms baseline approaches in accuracy

Approach replicates professional DJ cue point selection

Abstract

The automatic identification of cue points is a central task in applications as diverse as music thumbnailing, mash-ups generation, and DJ mixing. Our focus lies in electronic dance music and in specific cue points, the "switch points", that make it possible to automatically construct transitions among tracks, mimicking what professional DJs do. We present an approach for the detection of switch points that embody a few general rules we established from interviews with professional DJs; the implementation of these rules is based on features extraction and novelty analysis. The quality of the generated switch points is assessed both by comparing them with a manually annotated dataset that we curated, and by evaluating them individually. We found that about 96\% of the points generated by our methodology are of good quality for use in a DJ mix.