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Segmentation parole/musique par Machinesà Vecteurs de Support

Mathieu Ramona Gael Richard 1, 2 
1 S2A - Signal, Statistique et Apprentissage
LTCI - Laboratoire Traitement et Communication de l'Information
Abstract : We compare in this paper diverse hierarchical and multi-class approaches for the speech/music segmentation task, based on Support Vector Machines, combined with a median filter post-processing. We show the advantage of the multi-class approaches over the hierarchical schemes evaluated. Quantitative results provide a F-mesure over 96% that largely exceeds the results gathered by the ESTER evaluation campaign. We also show the relevance of the SVM with very low feature vector dimension on this task.
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Submitted on : Friday, February 26, 2021 - 6:49:45 PM
Last modification on : Tuesday, October 19, 2021 - 11:16:31 AM


  • HAL Id : hal-03153896, version 1



Mathieu Ramona, Gael Richard. Segmentation parole/musique par Machinesà Vecteurs de Support. Journées d'Etude de la Parole (JEP), 2008, Avignon, France. ⟨hal-03153896⟩



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