Dear subscribers of the colloquium newsletter,

we are happy to inform you about the next date of our Communication Technology Colloquium.

Wednesday, April 10, 2024
Speaker: Henning Konermann
Time: 2:30 p.m.
Location: hybrid - Lecture room 4G and
https://rwth.zoom.us/j/61215027648?pwd=MTJvayt5bkdka04raWZVempPZGE0Zz09

                    Meeting-ID: 612 1502 7648
                    Passwort: 380386

Master-Lecture:

Investigations on Phase-Aware Speech Enhancement Using Deep Neural Networks

Speech enhancement aims to improve speech quality and intelligibility by removing noise from noisy speech signals. Currently Machine Learning (ML) based speech enhancement has become mainstream and is used in hundreds of millions of devices. This is crucial in various applications, from telecommunications to hearing aids. Historically, the phase component was considered unimportant for this task when using the analysis-modification-synthesis approach. However, with the rise of ML and, in particular, Deep Neural Networks (DNNs), these technologies have become increasingly important in recent times. This thesis presents an in-depth study of phase-aware speech enhancement using DNNs, initially focusing on the theoretical benefits of integrating phase information into the speech enhancement process through oracle experiments. A significant emphasis of this work is on the recently proposed Consistent-Inconsistent Phase (CIP) approach, discussing its advantages and disadvantages to phase estimation. Traditional magnitude estimation, with and without additional phase information, serves as the baseline for comparison. It has been demonstrated that CIP offers theoretical advantages over pure phase estimation and could, in theory, perform equally well as magnitude estimation without additional phase information while adopting the noisy phase for synthesis. However, the practical implementation does not fully realize its theoretical potential when validating the theoretical results by replicating the experiments with state-of-the-art DNNs. Solely in the context of background noise removal, a combination of magnitude and CIP estimation proves clear superiority to other techniques evaluated in this study. The estimation of the CIP emerges as a viable alternative to direct estimation of the clean phase, especially in noise-dominated signals.

All interested parties are cordially invited, registration is not required.

Simone Sedgwick
Secretariat
Institute of Communication Systems(IKS)
Prof. Dr.-Ing. Peter Jax
RWTH Aachen University
Muffeter Weg 3a, 52074 Aachen, Germany
+49 241 80 26956(phone)
+49 241 80 22254(fax) 
sedgwick@iks.rwth-aachen.de
https://www.iks.rwth-aachen.de/