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dc.contributor.authorBerrah, Lynda-
dc.contributor.authorMendjel, Nacira-
dc.contributor.otherBelouchrani, Adel, Directeur de thèse-
dc.contributor.otherTebache, Soufiane, Directeur de thèse-
dc.descriptionMémoire de Projet de Fin d’Études : Électronique : Alger, École Nationale Polytechnique : 2022 Mémoire confidentiel jusqu'à juillet 2024fr_FR
dc.description.abstractIn a real-world environment, microphones record not only the target speech signal but also other available sources, the room acoustic effects, and background noise. Hence, extracting target speech from noisy convolutive mixtures is highly desirable for many applictions. This work aims to address the convolutive blind source separation of speech signals. First, we studied and compared three frequency-domain blind speech separation algorithms: IVA, Fast IVA, and ILRMA. Then, we worked on improving the performances of these algorithms using two different post-processings: speech denoising and SIMO equalization. The results demonstrate a significant improvement in performance. Finally, the selected separation scheme was implemented on an embedded system and tested on real-world signalsfr_FR
dc.subjectBlind speech separationfr_FR
dc.subjectFast IVAfr_FR
dc.subjectSIMO equalizationfr_FR
dc.subjectDenoising and embedded systemsfr_FR
dc.titleBlind speech separation : algorithm improvement and implementation using raspberry pi with uma-8-sp mic array testbedfr_FR
Collection(s) :Département Electronique

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