Dear subscribers of the colloquium newsletter,

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

Thursday, March 9, 2023
Speaker: Maximilian Tillmann
Time: 2:00 p.m.
Location: hybrid - Lecture room 4G and https://rwth.zoom.us/j/97904157921?pwd=SWpsbDl0MWhrWjY1ZkZaeFRoYmErZz09

Meeting-ID: 979 0415 7921
Passwort: 481650

Master-Lecture: Investigations on Autoencoder Models for Online System Identification

Speech communication devices are indispensable in our daily work and personal lives. Using
them in hands free mode can create an echo signal, which, if no action is taken, would disturb
the speaker. However, the echo signal can be predicted, when the impulse response between
loudspeaker and microphone is known. For this task, system identification algorithms exist,
such as the Least-Mean-Square (LMS ) algorithm, the Normalized-Least-Mean-Square (NLMS )
algorithm, and the Kalman filter. They work well in general, but face difficulties when
confronted with high correlation input signals, high noise levels, or rapidly changing impulse
responses over time.

This thesis aims to explore whether prior knowledge about the impulse response can improve
system identification. The key approach is to utilize the manifold hypothesis, which has
shown promising results in previous works in mapping acoustic room impulse responses to
a lower dimensional subspace. These approaches require training data of impulse responses.
This thesis investigates how well affine subspace models can represent impulse response with
a limited number of subspace components compared to the same number of components in
the time domain. One well known way to find an optimal affine subspace is by Principal-
Component-Analysis (PCA). It is shown that the affine subspace model can have the same
achievable system mismatch with significantly less number of subspace components, when the
loudspeaker and the microphone are constrained in their positions.

The manifold LMS algorithm, the manifold NLMS algorithm and the manifold Kalman filter
are proposed in this thesis, which can utilise general non linear manifolds for the acoustic
echo compensation task. For the manifold LMS and NLMS algorithm in the case of white
noise excitation and an affine manifold, the expected convergence speed and the expected
steady state system mismatch are derived theoretically and are shown to accurately describe
the algorithms behaviour in simulations. For scenarios with constrained loudspeaker and
microphone positions it is shown that the manifold NLMS algorithm significantly outperforms
the time domain NLMS algorithm. The manifold Kalman filter is compared to the time
domain Kalman filter and another subspace approach from literature.

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

General information on the colloquium, as well as a current list of dates of the Communication Technology Colloquium can be found at:
https://www.iks.rwth-aachen.de/aktuelles/kolloquium

-- 
Simone Sedgwick
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/