Landmark-Based Audio Fingerprinting for DJ Mix Monitoring

Recently, the media monitoring industry shows increased interest in applying automated audio identification systems for revenue distribution of DJ performances played in discotheques. DJ mixes incorporate a wide variety of signal modifications, e.g. pitch shifting, tempo modifications, cross-fading and beat-matching. These signal modifications are expected to be more severe than what is usually encountered in the monitoring of radio and TV broadcasts. The monitoring of DJ mixes presents a hard challenge for automated music identification systems, which need to be robust to various signal modifications while maintaining a high level of specificity to avoid false revenue assignment. In this work we assess the fitness of three landmark-based audio fingerprinting systems with different properties on real-world data – DJ mixes that were performed in discotheques. To enable the research community to evaluate systems on DJ mixes, we also create and publish a freely available, creative-commons licensed dataset of DJ mixes along with their reference tracks and song-border annotations. Experiments on these datasets reveal that a recent quad-based method achieves considerably higher performance on this task than the other methods.