| Titre : |
Blind speech separation : algorithm improvement and implementation using raspberry pi with uma-8-sp mic array testbed |
| Type de document : |
document électronique |
| Auteurs : |
Lynda Berrah, Auteur ; Nacira Mendjel, Auteur ; Adel Belouchrani, Directeur de thèse ; Soufiane Tebache, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2022 |
| Importance : |
1 fichier PDF (4.23 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Électronique : Alger, École Nationale Polytechnique : 2022
Bibliogr. f. 110 - 116 .- Annexe f. 117 - 118 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Blind speech separation IVA Fast ILRMA SIMO equalization Denoising and embedded systems |
| Index. décimale : |
PN01022 |
| Résumé : |
In 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 signals |
Blind speech separation : algorithm improvement and implementation using raspberry pi with uma-8-sp mic array testbed [document électronique] / Lynda Berrah, Auteur ; Nacira Mendjel, Auteur ; Adel Belouchrani, Directeur de thèse ; Soufiane Tebache, Directeur de thèse . - [S.l.] : [s.n.], 2022 . - 1 fichier PDF (4.23 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Électronique : Alger, École Nationale Polytechnique : 2022
Bibliogr. f. 110 - 116 .- Annexe f. 117 - 118 Langues : Anglais ( eng)
| Mots-clés : |
Blind speech separation IVA Fast ILRMA SIMO equalization Denoising and embedded systems |
| Index. décimale : |
PN01022 |
| Résumé : |
In 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 signals |
|
PN00100 
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