Dear subscribers of the colloquium newsletter,
we are happy to inform you about the next date of our Communication
Technology Colloquium.
*Monday, June 13, 2022*
*Speaker*: Zihang Wei
*Time*: 02:00 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 Supervised System Identification
Algorithms
The system identification task aims at inferring the room impulse
response of a specific acoustic enclosure. System identification is
mandatory in applications such as acoustic echo cancellation and cross
talk cancellation. Traditional gradient-based algorithms such as
normalized least mean square algorithm uses FIR filters to estimate the
RIRs, unfortunately, in a relatively high dimension. A novel dual-stage
algorithm is proposed in this thesis. The algorithm performs a state
update where allowed states are located on a manifold. In a first stage,
an undercomplete autoencoder is trained over the RIR data set. In the
second stage, we perform the system identification tasks. Here the
problem is reformulated such that the latent state is updated instead of
the full impulse response. The trained decoder is then exploited to
transform the latent variables to a proper impulse response. Evaluation
is made between the reconstructed RIR with reference to the true RIR.
In this thesis, at first, the simulation framework generates RIR data
set. Then autoencoders with different layer setups are trained on the
generated data set. The qualified autoencoders are employed in the
inference stage to perform the system identification tasks. Two crucial
parameters, i. e., the latent dimension size and updating step size of
the manifold are investigated under different SNR conditions. It is
demonstrated that under noisy conditions, the proposed method
outperforms the traditional NLMS approach. Evaluation results also show
that lower bottleneck size design benefits the system identification
with adverse noise
and
*Monday, June 13, 2022*
*Speaker*: Johannes Imort
*Time*: 03:00 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*: Online Learning of Loudspeaker Nonlineraities for
Acoustic Echo Cancellation
Hands-free communication is pervasive throughout modern society,
requiring a robust cancellation of the echo from the far end speech
signal that is emitted from the loudspeaker to the microphone. Acoustic
echo cancellation addresses this issue typically by employing linear
adaptive filters. However, in reality, the echo path is nonlinear due to
the non-ideal characteristics of acoustic transducers and power
amplifiers operated at their physical limits.
This thesis introduces and investigates a novel approach to tackle
nonlinear AEC by estimating the nonlinear reference signal using a deep
neural network and a differentiable Kalman filter. The hybrid system is
designed to learn loudspeaker nonlinearities directly from data,
enabling end-to-end training on data composed of pairs of the far end
reference and near end microphone signals. In contrast to previous
neural network-based solutions that have been tailored toward one
particular loudspeaker, the proposed system aims to be generalizable for
different loudspeaker nonlinearities. Therefore, inspired by linear
adaptive filtering, the recurrent architecture explicitly takes
advantage of the information in the residual echo in order to estimate
the nonlinearity adaptively.
The proposed approach was evaluated for both simulated and measured
data. The results indicate that the architecture could enable faster
convergence and better steady state performance than related adaptive
approaches. Furthermore, some examples demonstrated that the performance
of a method that makes use of oracle knowledge could be surpassed,
evidently because the models adapt to the linear acoustic echo path, too.
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
--
Irina Esser
Institute of Communication Systems (IKS)
RWTH Aachen University
Muffeter Weg 3a, 52074 Aachen, Germany
+49 241 80 26958 (phone)
ronkartz(a)iks.rwth-aachen.de
http://www.iks.rwth-aachen.de/