Criteria-based comparison of Systems Engineering and agile methods - Humpert, Lynn; Mundt, Enrik; Bretz, Lukas; Bernz, Vanessa; Anacker, Harald; Dumitrescu, Roman (8th International Conference on Information Systems Engineering)

Strategic planning of the collaboration between humans and artificial intelligence in production - Gabriel, Stefan; Kühn, Arno; Dumitrescu, Roman (Procedia CIRP)

Principles for the effective application of Systems Engineering: A systematic literature review and application use case - Mundt, Enrik; Wilke, Daria; Anacker, Harald; Dumitrescu, Roman

Integration von Design Thinking in Systems Engineering mit Hilfe des Systemdenkens - Tekaat, Julian; Wilke, Daria; Anacker, Harald; Dumitrescu, Roman

Re-examining the quality dimensions of synthetic speech - Seebauer, Fritz; Kuhlmann, Michael; Haeb-Umbach, Reinhold; Wagner, Petra (12th Speech Synthesis Workshop (SSW) 2023)

Accelerating the analysis of optical quantum systems using the Koopman operator - Peitz, Sebastian; Hunstig, Anna; Rose, Hendrik; Meier, Torsten
The prediction of photon echoes is an important technique for gaining an understanding of optical quantum systems. However, this requires a large number of simulations with varying parameters and/or input pulses, which renders numerical studies expensive. This article investigates how we can use data-driven surrogate models based on the Koopman operator to accelerate this process. In order to be successful, we require a model that is accurate over a large number of time steps. To this end, we employ a bilinear Koopman model using extended dynamic mode decomposition and simulate the optical Bloch equations for an ensemble of inhomogeneously broadened two-level systems. Such systems are well suited to describe the excitation of excitonic resonances in semiconductor nanostructures, for example, ensembles of semiconductor quantum dots. We perform a detailed study on the required number of system simulations such that the resulting data-driven Koopman model is sufficiently accurate for a wide range of parameter settings. We analyze the L2 error and the relative error of the photon echo peak and investigate how the control positions relate to the stabilization. After proper training, the dynamics of the quantum ensemble can be predicted accurately and numerically very efficiently by our methods.

Combined Economic and Emission Dispatch of a Microgrid Considering Multiple Generators - Philipo, Godiana Hagile; Kakande, Josephine Nakato; Krauter, Stefan (Proceedings of the 2023 IEEE PES/IAS PowerAfrica Conference)

Demand side management potential of refrigeration appliances - Kakande, Josephine Nakato; Philipo, Godiana Hagile; Krauter, Stefan (Proceedings of the 2023 IEEE PES/IAS PowerAfrica Conference)

Demand-Side-Management for Optimal dispatch of an Isolated Solar Microgrid - Philipo, Godiana Hagile; Kakande, Josephine Nakato; Krauter, Stefan (Proceedings of the 2023 IEEE AFRICON, Nairobi, Kenya)

Segment-Less Continuous Speech Separation of Meetings: Training and Evaluation Criteria - von Neumann, Thilo; Kinoshita, Keisuke; Boeddeker, Christoph; Delcroix, Marc; Haeb-Umbach, Reinhold (IEEE/ACM Transactions on Audio, Speech, and Language Processing)
Continuous Speech Separation (CSS) has been proposed to address speech overlaps during the analysis of realistic meeting-like conversations by eliminating any overlaps before further processing. CSS separates a recording of arbitrarily many speakers into a small number of overlap-free output channels, where each output channel may contain speech of multiple speakers. This is often done by applying a conventional separation model trained with Utterance-level Permutation Invariant Training (uPIT), which exclusively maps a speaker to an output channel, in sliding window approach called stitching. Recently, we introduced an alternative training scheme called Graph-PIT that teaches the separation network to directly produce output streams in the required format without stitching. It can handle an arbitrary number of speakers as long as never more of them overlap at the same time than the separator has output channels. In this contribution, we further investigate the Graph-PIT training scheme. We show in extended experiments that models trained with Graph-PIT also work in challenging reverberant conditions. Models trained in this way are able to perform segment-less CSS, i.e., without stitching, and achieve comparable and often better separation quality than the conventional CSS with uPIT and stitching. We simplify the training schedule for Graph-PIT with the recently proposed Source Aggregated Signal-to-Distortion Ratio (SA-SDR) loss. It eliminates unfavorable properties of the previously used A-SDR loss and thus enables training with Graph-PIT from scratch. Graph-PIT training relaxes the constraints w.r.t. the allowed numbers of speakers and speaking patterns which allows using a larger variety of training data. Furthermore, we introduce novel signal-level evaluation metrics for meeting scenarios, namely the source-aggregated scale- and convolution-invariant Signal-to-Distortion Ratio (SA-SI-SDR and SA-CI-SDR), which are generalizations of the commonly used SDR-based metrics for the CSS case.
Keywords: Continuous Speech Separation; Source Separation; Graph-PIT; Dynamic Programming; Permutation Invariant Training

On Word Error Rate Definitions and Their Efficient Computation for Multi-Speaker Speech Recognition Systems - von Neumann, Thilo; Boeddeker, Christoph; Kinoshita, Keisuke; Delcroix, Marc; Haeb-Umbach, Reinhold (ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))
We propose a general framework to compute the word error rate (WER) of ASR systems that process recordings containing multiple speakers at their input and that produce multiple output word sequences (MIMO). Such ASR systems are typically required, e.g., for meeting transcription. We provide an efficient implementation based on a dynamic programming search in a multi-dimensional Levenshtein distance tensor under the constraint that a reference utterance must be matched consistently with one hypothesis output. This also results in an efficient implementation of the ORC WER which previously suffered from exponential complexity. We give an overview of commonly used WER definitions for multi-speaker scenarios and show that they are specializations of the above MIMO WER tuned to particular application scenarios. We conclude with a discussion of the pros and cons of the various WER definitions and a recommendation when to use which.
Keywords: Word Error Rate; Meeting Recognition; Levenshtein Distance

MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems - von Neumann, Thilo; Boeddeker, Christoph; Delcroix, Marc; Haeb-Umbach, Reinhold (Proc. CHiME 2023 Workshop on Speech Processing in Everyday Environments)
MeetEval is an open-source toolkit to evaluate all kinds of meeting transcription systems. It provides a unified interface for the computation of commonly used Word Error Rates (WERs), specifically cpWER, ORC WER and MIMO WER along other WER definitions. We extend the cpWER computation by a temporal constraint to ensure that only words are identified as correct when the temporal alignment is plausible. This leads to a better quality of the matching of the hypothesis string to the reference string that more closely resembles the actual transcription quality, and a system is penalized if it provides poor time annotations. Since word-level timing information is often not available, we present a way to approximate exact word-level timings from segment-level timings (e.g., a sentence) and show that the approximation leads to a similar WER as a matching with exact word-level annotations. At the same time, the time constraint leads to a speedup of the matching algorithm, which outweighs the additional overhead caused by processing the time stamps.
Keywords: Speech Recognition; Word Error Rate; Meeting Transcription

Class Expression Learning with Multiple Representations - Ngonga Ngomo, Axel-Cyrille; Demir, Caglar; Kouagou, N'Dah Jean; Heindorf, Stefan; Karalis, Nikoloas; Bigerl, Alexander (Compendium of Neurosymbolic Artificial Intelligence)

Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks - Gburrek, Tobias; Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (Proc. Asilomar Conference on Signals, Systems, and Computers)
We propose a diarization system, that estimates “who spoke when” based on spatial information, to be used as a front-end of a meeting transcription system running on the signals gathered from an acoustic sensor network (ASN). Although the spatial distribution of the microphones is advantageous, exploiting the spatial diversity for diarization and signal enhancement is challenging, because the microphones’ positions are typically unknown, and the recorded signals are initially unsynchronized in general. Here, we approach these issues by first blindly synchronizing the signals and then estimating time differences of arrival (TDOAs). The TDOA information is exploited to estimate the speakers’ activity, even in the presence of multiple speakers being simultaneously active. This speaker activity information serves as a guide for a spatial mixture model, on which basis the individual speaker’s signals are extracted via beamforming. Finally, the extracted signals are forwarded to a speech recognizer. Additionally, a novel initialization scheme for spatial mixture models based on the TDOA estimates is proposed. Experiments conducted on real recordings from the LibriWASN data set have shown that our proposed system is advantageous compared to a system using a spatial mixture model, which does not make use of external diarization information.
Keywords: Diarization; time difference of arrival; ad-hoc acoustic sensor network; meeting transcription

Post-Processing Independent Evaluation of Sound Event Detection Systems - Ebbers, Janek; Haeb-Umbach, Reinhold; Serizel, Romain (Proceedings of the 8th Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023))
Due to the high variation in the application requirements of sound event detection (SED) systems, it is not sufficient to evaluate systems only in a single operating mode. Therefore, the community recently adopted the polyphonic sound detection score (PSDS) as an evaluation metric, which is the normalized area under the PSD receiver operating characteristic (PSD-ROC). It summarizes the system performance over a range of operating modes resulting from varying the decision threshold that is used to translate the system output scores into a binary detection output. Hence, it provides a more complete picture of the overall system behavior and is less biased by specific threshold tuning. However, besides the decision threshold there is also the post-processing that can be changed to enter another operating mode. In this paper we propose the post-processing independent PSDS (piPSDS) as a generalization of the PSDS. Here, the post-processing independent PSD-ROC includes operating points from varying post-processings with varying decision thresholds. Thus, it summarizes even more operating modes of an SED system and allows for system comparison without the need of implementing a post-processing and without a bias due to different post-processings. While piPSDS can in principle combine different types of post-processing, we here, as a first step, present median filter independent PSDS (miPSDS) results for this year’s DCASE Challenge Task4a systems. Source code is publicly available in our sed_scores_eval package (https://github.com/fgnt/sed_scores_eval).

"Would You Give the Same Priority to the Bank and a Game? I Do Not!" Exploring Credential Management Strategies and Obstacles during Password Manager Setup - Amft, Sabrina; Höltervennhoff, Sandra; Huaman, Nicolas; Acar, Yasemin; Fahl, Sascha (Nineteenth Symposium on Usable Privacy and Security, SOUPS 2023, Anaheim, CA, USA, August 5-7, 2023)

"I wouldn’t want my unsafe code to run my pacemaker": An Interview Study on the Use, Comprehension, and Perceived Risks of Unsafe Rust - Höltervennhoff, Sandra; Klostermeyer, Philip; Wöhler, Noah; Acar, Yasemin; Fahl, Sascha (32nd USENIX Security Symposium, USENIX Security 2023, Anaheim, CA, USA, August 9-11, 2023)

Ethical Frameworks and Computer Security Trolley Problems: Foundations for Conversations - Kohno, Tadayoshi; Acar, Yasemin; Loh, Wulf (32nd USENIX Security Symposium, USENIX Security 2023, Anaheim, CA, USA, August 9-11, 2023)

Who Comes Up with this Stuff? Interviewing Authors to Understand How They Produce Security Advice - Neil, Lorenzo; Sri Ramulu, Harshini; Acar, Yasemin; Reaves, Bradley (Nineteenth Symposium on Usable Privacy and Security, SOUPS 2023, Anaheim, CA, USA, August 5-7, 2023)

Beyond the Boolean: How Programmers Ask About, Use, and Discuss Gender - Bouma-Sims, Elijah; Acar, Yasemin (Proc. ACM Hum. Comput. Interact.)