| Titre : |
UAV speech control for human drone interaction |
| Type de document : |
document électronique |
| Auteurs : |
Amina Boulkout, Auteur ; Chérif Larbes, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2023 |
| Importance : |
1 fichier PDF (11 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Etudes : Electronique : Alger, Ecole Nationale Polytechnique : 2023
Bibliogr. p. 88 - 89 . - Webographie p. 90 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
DNN
Human-Drone Interaction
SEGAN
Speech recognition
UAV |
| Index. décimale : |
PN00823 |
| Résumé : |
In recent years, the significance of human-drone interaction in scientific research has grown substantially. When engaging with drones, humans undertake various responsibilities, which are contingent upon the drone’s application and level of autonomy. This study aims to regulate the movements of unmanned aerial vehicles (UAVs) by utilizing a keyword spotting system. To achieve this, a deep neural network (DNN) is trained to comprehend user speech in both noisy and noiseless environments and generate the desired control commands accordingly. The hardware implementation of the developed system demonstrates both high accuracy in speech recognition and ease of control. |
UAV speech control for human drone interaction [document électronique] / Amina Boulkout, Auteur ; Chérif Larbes, Directeur de thèse . - [S.l.] : [s.n.], 2023 . - 1 fichier PDF (11 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Etudes : Electronique : Alger, Ecole Nationale Polytechnique : 2023
Bibliogr. p. 88 - 89 . - Webographie p. 90 Langues : Anglais ( eng)
| Mots-clés : |
DNN
Human-Drone Interaction
SEGAN
Speech recognition
UAV |
| Index. décimale : |
PN00823 |
| Résumé : |
In recent years, the significance of human-drone interaction in scientific research has grown substantially. When engaging with drones, humans undertake various responsibilities, which are contingent upon the drone’s application and level of autonomy. This study aims to regulate the movements of unmanned aerial vehicles (UAVs) by utilizing a keyword spotting system. To achieve this, a deep neural network (DNN) is trained to comprehend user speech in both noisy and noiseless environments and generate the desired control commands accordingly. The hardware implementation of the developed system demonstrates both high accuracy in speech recognition and ease of control. |
|
PN00100 
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