  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.
