Djamaleddine Berrabah
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Djamaleddine Berrabah has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Adil Bakri
Scientific and Technical Research Centre for Arid Regions (CRSTRA), Biskra
1 shared publication - Boumediene Khaouani
Scientific and Technical Research Centre for Arid Regions (CRSTRA), Biskra
1 shared publication
Latest Publications
- Open Access
- 12 July 2026
- Published by University Tahri Mohamed of Bechar in Journal of Elecetrical Engineering and Renewable Energies Development
- Vol. 2 (2) , 54-59
Packet Loss Concealment Using Deep Neural Networks for CS-CELP-Type Speech Coders in VoIP
Packet loss in Voice over Internet Protocol (VoIP) communications can significantly degrade speech quality, particularly affecting Conjugate-Structure Code-Excited Linear Prediction (CS-CELP) type speech coders like G.729. Traditional Packet Loss Concealment (PLC) methods often struggle with consecutive frame losses. Recent advancements in deep learning have introduced Deep Neural Networks (DNNs) as a promising solution for PLC. This article explores the integration of DNNs into PLC for CS-CELP-type speech coders, focusing on methodologies, experimental setups, results, and future directions.
- 12 July 2026
- Published by University Tahri Mohamed of Bechar in Journal of Elecetrical Engineering and Renewable Energies Development
- Vol. 2 (2) , 54-59
Open AccessPacket Loss Concealment Using Deep Neural Networks for CS-CELP-Type Speech Coders in VoIP
Packet loss in Voice over Internet Protocol (VoIP) communications can significantly degrade speech quality, particularly affecting Conjugate-Structure Code-Excited Linear Prediction (CS-CELP) type speech coders like G.729. Traditional Packet Loss Concealment (PLC) methods often struggle with consecutive frame losses. Recent advancements in deep learning have introduced Deep Neural Networks (DNNs) as a promising solution for PLC. This article explores the integration of DNNs into PLC for CS-CELP-type speech coders, focusing on methodologies, experimental setups, results, and future directions.