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 mod...

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Bibliographic Details
Main Authors: Plotz, T., Fink, G.A., Husemann, P., Kanies, S., Lienemann, K., Marschall, T., Martin, M., Schillingmann, L., Steinrucken, M., Sudek, H.
Format: Conference Proceeding
Language:English
Subjects:
Acoustic signal detection
CD recording
Digital recording
Hidden Markov models
Law
Legal factors
Multiple signal classification
Music
Pattern recognition
Stochastic processes
Online Access:Request full text
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Summary: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 obtained 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
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2006.297