Flusseinsamkeiten: Hölderlins 'Ister' und 'Nekar' im Berlin der Gegenwart bei Tomer Dotan-Dreyfus - Spener, Anna Maria

Utterance-by-utterance overlap-aware neural diarization with Graph-PIT - Kinoshita, Keisuke; von Neumann, Thilo; Delcroix, Marc; Boeddeker, Christoph; Haeb-Umbach, Reinhold (Proc. Interspeech 2022)
Recent speaker diarization studies showed that integration of end-to-end neural diarization (EEND) and clustering-based diarization is a promising approach for achieving state-of-the-art performance on various tasks. Such an approach first divides an observed signal into fixed-length segments, then performs {\it segment-level} local diarization based on an EEND module, and merges the segment-level results via clustering to form a final global diarization result. The segmentation is done to limit the number of speakers in each segment since the current EEND cannot handle a large number of speakers. In this paper, we argue that such an approach involving the segmentation has several issues; for example, it inevitably faces a dilemma that larger segment sizes increase both the context available for enhancing the performance and the number of speakers for the local EEND module to handle. To resolve such a problem, this paper proposes a novel framework that performs diarization without segmentation. However, it can still handle challenging data containing many speakers and a significant amount of overlapping speech. The proposed method can take an entire meeting for inference and perform {\it utterance-by-utterance} diarization that clusters utterance activities in terms of speakers. To this end, we leverage a neural network training scheme called Graph-PIT proposed recently for neural source separation. Experiments with simulated active-meeting-like data and CALLHOME data show the superiority of the proposed approach over the conventional methods.

SA-SDR: A Novel Loss Function for Separation of Meeting Style Data - von Neumann, Thilo; Kinoshita, Keisuke; Boeddeker, Christoph; Delcroix, Marc; Haeb-Umbach, Reinhold (ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator - Cord-Landwehr, Tobias; von Neumann, Thilo; Boeddeker, Christoph; Haeb-Umbach, Reinhold (2022 International Workshop on Acoustic Signal Enhancement (IWAENC))
The scope of speech enhancement has changed from a monolithic view of single, independent tasks, to a joint processing of complex conversational speech recordings. Training and evaluation of these single tasks requires synthetic data with access to intermediate signals that is as close as possible to the evaluation scenario. As such data often is not available, many works instead use specialized databases for the training of each system component, e.g WSJ0-mix for source separation. We present a Multi-purpose Multi-Speaker Mixture Signal Generator (MMS-MSG) for generating a variety of speech mixture signals based on any speech corpus, ranging from classical anechoic mixtures (e.g., WSJ0-mix) over reverberant mixtures (e.g., SMS-WSJ) to meeting-style data. Its highly modular and flexible structure allows for the simulation of diverse environments and dynamic mixing, while simultaneously enabling an easy extension and modification to generate new scenarios and mixture types. These meetings can be used for prototyping, evaluation, or training purposes. We provide example evaluation data and baseline results for meetings based on the WSJ corpus. Further, we demonstrate the usefulness for realistic scenarios by using MMS-MSG to provide training data for the LibriCSS database.

Monaural source separation: From anechoic to reverberant environments - Cord-Landwehr, Tobias; Boeddeker, Christoph; von Neumann, Thilo; Zorila, Catalin; Doddipatla, Rama; Haeb-Umbach, Reinhold (2022 International Workshop on Acoustic Signal Enhancement (IWAENC))
Impressive progress in neural network-based single-channel speech source separation has been made in recent years. But those improvements have been mostly reported on anechoic data, a situation that is hardly met in practice. Taking the SepFormer as a starting point, which achieves state-of-the-art performance on anechoic mixtures, we gradually modify it to optimize its performance on reverberant mixtures. Although this leads to a word error rate improvement by 7 percentage points compared to the standard SepFormer implementation, the system ends up with only marginally better performance than a PIT-BLSTM separation system, that is optimized with rather straightforward means. This is surprising and at the same time sobering, challenging the practical usefulness of many improvements reported in recent years for monaural source separation on nonreverberant data.

Couleur Locale - Schulze, Max (Die Wirklichkeit ist sowieso da)

Single Club - Schulze, Max (Düsseldorf und Fotografie)

Rezension von: Viola Schrader: Historisches Denken und sprachliches Handeln. Eine qualitativ-empirische Untersuchung von Schülertexten. Münster et al.: LIT 2021 - Hartung, Olaf (sehepunkte)

‚Schreibsensibel‘ Geschichte unterrichten: Fachspezifische Textkompetenz als Gegenstand und Medium des historischen Lernens - Hartung, Olaf; Memminger, Josef (Schreiben fachübergreifend fördern. Grundlagen und Praxisanregungen für Schule, Unterricht und Lehrerinnen- und Lehrerbildung)

On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-Varying Sampling Rate Offsets and Speaker Changes - Gburrek, Tobias; Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall - Letmathe, Sebastian; Feng, Yuanhua; Uhde, André (Journal of Risk)
In this paper new semiparametric generalized autoregressive conditional heteroscedasticity (GARCH) models with long memory are introduced. A multiplicative decomposition of the volatility into a conditional component and an unconditional component is assumed. The estimation of the latter is carried out by means of a data-driven local polynomial smoother. According to the revised recommendations by the Basel Committee on Banking Supervision to measure market risk in the banks’ trading books, these new semiparametric GARCH models are applied to obtain rolling one-step ahead forecasts for the value-at-risk and expected shortfall (ES) for market risk assets. Standard regulatory traffic-light tests and a newly introduced traffic-light test for the ES are carried out for all models. In addition, model performance is assessed via a recently introduced model selection criterion. The practical relevance of our proposal is demonstrated by a comparative study. Our results indicate that semiparametric long-memory GARCH models are a meaningful substitute for their conventional, parametric counterparts.
Keywords: long memory; generalized autoregressive conditional heteroscedasticity (GARCH) models; value-at-risk (VaR); expected shortfall (ES); traffic-light test; backtesting

Warping of Radar Data Into Camera Image for Cross-Modal Supervision in Automotive Applications - Grimm, Christopher; Fei, Tai; Warsitz, Ernst; Farhoud, Ridha; Breddermann, Tobias; Haeb-Umbach, Reinhold (IEEE Transactions on Vehicular Technology)
We present an approach to automatically generate semantic labels for real recordings of automotive range-Doppler (RD) radar spectra. Such labels are required when training a neural network for object recognition from radar data. The automatic labeling approach rests on the simultaneous recording of camera and lidar data in addition to the radar spectrum. By warping radar spectra into the camera image, state-of-the-art object recognition algorithms can be applied to label relevant objects, such as cars, in the camera image. The warping operation is designed to be fully differentiable, which allows backpropagating the gradient computed on the camera image through the warping operation to the neural network operating on the radar data. As the warping operation relies on accurate scene flow estimation, we further propose a novel scene flow estimation algorithm which exploits information from camera, lidar and radar sensors. The proposed scene flow estimation approach is compared against a state-of-the-art scene flow algorithm, and it outperforms it by approximately 30% w.r.t. mean average error. The feasibility of the overall framework for automatic label generation for RD spectra is verified by evaluating the performance of neural networks trained with the proposed framework for Direction-of-Arrival estimation.

Erfolgreiches People Management in KMU und Start-ups - Krebs, Benjamin; Kabst, Rüdiger (PERSONALquartely)

Methodenverliebt am Markt vorbei? - Rebert, Tobias; Tomin, Slawa; Wach, Bernhard; Kabst, Rüdiger (Intrapreneurship)

Pre-Training And Self-Training For Sound Event Detection In Domestic Environments - Ebbers, Janek; Haeb-Umbach, Reinhold
In this report we present our system for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Challenge Task 4: Sound Event Detection in Domestic Environments 1 . As in previous editions of the Challenge, we use forward-backward convolutional recurrent neural networks (FBCRNNs) [1, 2] for weakly labeled and semi-supervised sound event detection (SED) and eventually generate strong pseudo labels for weakly labeled and unlabeled data. Then, (tag-conditioned) bidirectional CRNNs (Bi-CRNNs) [1, 2] are trained in a strongly supervised manner as our final SED models. In each of the training stages we use multiple iterations of self-training. Compared to previous editions, we improved our system performance by 1) some tweaks regarding data augmentation, pseudo labeling and inference 2) using weakly labeled AudioSet data [3] for pretraining larger networks and 3) augmenting the DESED data [4] with strongly labeled AudioSet data [5] for finetuning of the networks. Source code is publicly available at https://github.com/fgnt/pb_sed.

If You Can’t Get Them to the Lab: Evaluating a Virtual Study Environment with Security Information Workers - Huaman, Nicolas; Krause, Alexander; Wermke, Dominik; Klemmer, Jan H.; Stransky, Christian; Acar, Yasemin; Fahl, Sascha (Eighteenth Symposium on Usable Privacy and Security, SOUPS 2022, Boston, MA, USA, August 7-9, 2022)

How Does Usable Security (Not) End Up in Software Products? Results From a Qualitative Interview Study - Gutfleisch, Marco; Klemmer, Jan H.; Busch, Niklas; Acar, Yasemin; Sasse, M. Angela; Fahl, Sascha (43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022)

27 Years and 81 Million Opportunities Later: Investigating the Use of Email Encryption for an Entire University - Stransky, Christian; Wiese, Oliver; Roth, Volker; Acar, Yasemin; Fahl, Sascha (43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022)

Where to Recruit for Security Development Studies: Comparing Six Software Developer Samples - Kaur, Harjot; Amft, Sabrina; Votipka, Daniel; Acar, Yasemin; Fahl, Sascha (31st USENIX Security Symposium, USENIX Security 2022, Boston, MA, USA, August 10-12, 2022)

They Would Do Better If They Worked Together: Interaction Problems Between Password Managers and the Web - Huaman, Nicolas; Amft, Sabrina; Oltrogge, Marten; Acar, Yasemin; Fahl, Sascha (IEEE Secur. Priv.)