Automatic Detection of Song Changes in Music Mixes Using Stochastic Models

The annotation of song changes in music mixes created by DJs or radio stations for direct access in digital recordings is, usually, a very tedious work. In order to support this process we developed an automatic song change detection method which can be used for arbitrary music mixes. Stochastic models are applied to music data aiming at their segmentation with respect to automatically abstract generic acoustic units. The local analysis of these stochastic music models provides hypotheses for song changes. Results of an experimental evaluation processing music mix data demonstrate the effectiveness of our method for supporting the annotation with respect to song changes.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Gernot A. Fink, Henner Sudek, Kai Lienemann, Lars Schillingmann, Marcel Martin, Matthias Steinrucken, Peter Husemann, Sven Kanies, Thomas Plötz, Tobias Marschall
• Keywords: Arbitrary Music Mixes, Computer Vision, ICPR 2006, Song Changes, Stochastic Music Models