Overview

• Focus on EDM and on cue in positions
• Own dataset of 134 tracks created with experts
• 3 rules
• Novelty. A switch point marks a position of high novelty in rhythmic density, loudness, timbre, and/or harmony.
• Sections detection. A switch point always occurs on the downbeat at the start of a period.
• Salience detection. The section following a switch point has to be able to stand on its own in the mix.
• 500ms window for computing accuracy

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.

Motivation

Architecture

Results

MSAF - 'olda' with 'pcp'

Further Thoughts

Feels too simplistic with the small dataset and focus on only cue in points. Not useful for our projects right now.

Links