Study of the Performance of CEEMDAN in Underdetermined Speech Separation
This work addresses audio source separation for speech processing applications, but it is incremental as it tests an existing method on specific conditions.
The study investigated the CEEMDAN algorithm for underdetermined speech separation, finding it can remove certain types of noise from speech but fails to separate speech signals from each other in cocktail party scenarios.
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 source separation to know the limits of its work. It concluded two conditions related to frequencies and amplitudes of mixed signals to be separated by CEEMDAN. The performance of the algorithm in separating noise from speech and separating speech signals from each other is studied. The research reached a conclusion that CEEMDAN can remove some types of noise from speech (speech improvement), and it cannot separate speech signals from each other (cocktail party). Simulation is done using Matlab environment and Noizeus database.