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Effective singing voice detection in popular music using ARMA filtering

: Lukashevich, Hanna; Gruhne, Matthias; Dittmar, Christian

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Université Bordeaux, Laboratoire Bordelais de Recherche en Informatique -LaBRI-, Bordeaux:
10th International Conference on Digital Audio Effects, DAFx-07. Proceedings : 10th to 15th of September 2007 Bordeaux, France
Bordeaux: Université Bordeaux, 2007
ISBN: 978-88-901479-1-3
International Conference on Digital Audio Effects (DAFX) <10, 2007, Bordeaux>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IDMT ()
vocal analysis; music segmentation

Locating singing voice segments is essential for convenient indexing, browsing and retrieval large music archives and catalogues. Furthermore, it is beneficial for automatic music transcription and annotations. The approach described in this paper uses Mel-Frequency Cepstral Coefficients in conjunction with Gaussian Mixture Models for discriminating two classes of data (instrumental music and singing voice with music background). Due to imperfect classification behavior, the categorization without additional post-processing tends to alternate within a very short time span, whereas singing voice tends to be continuous for several frames. Thus, various tests have been performed to identify a suitable decision function and corresponding smoothing methods. Results are reported by comparing the performance of straightforward likelihood based classifications vs. postprocessing with an autoregressive moving average filtering method.