1 citations · 2 across the 3 of their papers we have counts for
4 papers
Study of the Performance of CEEMDAN in Underdetermined Speech Separation
Rawad Melhem, Riad Hamadeh, Assef Jafar
The CEEMDAN algorithm is one of the modern methods used in the analysis of non-stationary signals. This research presents a study of the effectiveness of this method in audio sourc…
Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems
Rawad Melhem, Assef Jafar, Oumayma Al Dakkak
This paper addresses the challenge of speaker separation, which remains an active research topic despite the promising results achieved in recent years. These results, however, oft…
Improving Deep Attractor Network by BGRU and GMM for Speech Separation
Rawad Melhem, Assef Jafar, Riad Hamadeh
Deep Attractor Network (DANet) is the state-of-the-art technique in speech separation field, which uses Bidirectional Long Short-Term Memory (BLSTM), but the complexity of the DANe…
Towards Solving Cocktail-Party: The First Method to Build a Realistic Dataset with Ground Truths for Speech Separation
Rawad Melhem, Assef Jafar, Oumayma Al Dakkak
Speech separation is very important in real-world applications such as human-machine interaction, hearing aids devices, and automatic meeting transcription. In recent years, a sign…