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@iks.rwth-aachen.de
http://www.iks.rwth-aachen.de/