Automatic Detection of Cue Points for DJ Mixing
This addresses the need for automated music mixing tools for DJs and music producers, but it is incremental as it builds on existing novelty analysis methods.
The paper tackled the problem of automatically detecting cue points (switch points) in electronic dance music to enable seamless transitions between tracks, achieving about 96% good quality points for use in DJ mixing.
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.