TY - JOUR A1 - Axenie, Cristian A1 - Halilov, Ertan A1 - Main, Julian A1 - Weiss, David T1 - Edge neuro-statistical learning for event-based visual motion detection and tracking in roadside safety systems JF - Neuromorphic Computing and Engineering N2 - The Vision Zero Program's purpose is to reduce traffic-related fatalities and serious injuries while promoting equitable, safe, and healthy mobility for all. Ultimately, the challenge is to detect pedestrians during the day and especially at night to implement safety measures. The current study introduces an award-winning low-power solution employing neuromorphic visual sensing and hybrid neuro-statistical processing developed by the Technische Hochschule Nürnberg team for the TinyML Vision Zero San Jose Competition. The solution proposes a novel neuromorphic edge fusion of spiking neural networks and event-based expectation maximization for the detection and tracking of pedestrians and bicyclists. We provide a deployment-ready evaluation of the detection performance along with robustness, energy footprint, and weatherization while emphasizing the advantages of the neuro-statistical edge solution and its city-level scaling capabilities. Y1 - 2025 U6 - https://doi.org/10.1088/2634-4386/adcbcb SN - 2634-4386 PB - IOP Publishing ER - TY - JOUR A1 - Axenie, Cristian T1 - Antifragile control systems in neuronal processing: a sensorimotor perspective JF - Biological Cybernetics N2 - The stability–robustness–resilience–adaptiveness continuum in neuronal processing follows a hierarchical structure that explains interactions and information processing among the different time scales. Interestingly, using “canonical” neuronal computational circuits, such as Homeostatic Activity Regulation, Winner-Take-All, and Hebbian Temporal Correlation Learning, one can extend the behavior spectrum towards antifragility. Cast already in both probability theory and dynamical systems, antifragility can explain and define the interesting interplay among neural circuits, found, for instance, in sensorimotor control in the face of uncertainty and volatility. This perspective proposes a new framework to analyze and describe closed-loop neuronal processing using principles of antifragility, targeting sensorimotor control. Our objective is two-fold. First, we introduce antifragile control as a conceptual framework to quantify closed-loop neuronal network behaviors that gain from uncertainty and volatility. Second, we introduce neuronal network design principles, opening the path to neuromorphic implementations and transfer to technical systems. Y1 - 2025 U6 - https://doi.org/10.1007/s00422-025-01003-7 SN - 1432-0770 VL - 119 IS - 2-3 PB - Springer Nature ER - TY - JOUR A1 - Agchar, Ismael A1 - Baumann, Ilja A1 - Braun, Franziska A1 - Perez-Toro, Paula Andrea A1 - Riedhammer, Korbinian A1 - Trump, Sebastian A1 - Ullrich, Martin T1 - A Survey of Music Generation in the Context of Interaction N2 - In recent years, machine learning, and in particular generative adversarial neural networks (GANs) and attention-based neural networks (transformers), have been successfully used to compose and generate music, both melodies and polyphonic pieces. Current research focuses foremost on style replication (e.g., generating a Bach-style chorale) or style transfer (e.g., classical to jazz) based on large amounts of recorded or transcribed music, which in turn also allows for fairly straight-forward “performance” evaluation. However, most of these models are not suitable for human-machine co-creation through live interaction, neither is clear, how such models and resulting creations would be evaluated. This article presents a thorough review of music representation, feature analysis, heuristic algorithms, statistical and parametric modelling, and human and automatic evaluation measures, along with a discussion of which approaches and models seem most suitable for live interaction. Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2402.15294 ER - TY - JOUR A1 - Freisinger, Steffen A1 - Schneider, Fabian A1 - Herygers, Aaricia A1 - Georges, Munir A1 - Riedhammer, Korbinian ED - Pesak, Krisztina T1 - Unsupervised Multilingual Topic Segmentation of Video Lectures: What can Hierarchical Labels tell us about the Performance? N2 - The current shift from in-person to online education, e.g., through lecture videos, requires novel techniques for quickly searching for and navigating through media content. At this point, an automatic segmentation of the videos into thematically coherent units can be beneficial. Like in a book, the topics in an educational video are often structured hierarchically. There are larger topics, which in turn are divided into different subtopics. We thus propose a metric that considers the hierarchical levels in the reference segmentation when evaluating segmentation algorithms. In addition, we propose a multilingual, unsupervised topic segmentation approach and evaluate it on three datasets with English, Portuguese and German lecture videos. We achieve WindowDiff scores of up to 0.373 and show the usefulness of our hierarchical metric. Y1 - 2023 U6 - https://doi.org/10.21437/slate.2023-27 ER - TY - JOUR A1 - Wagner, Dominik A1 - Baumann, Ilja A1 - Bocklet, Tobias ED - Baumann, Ilja T1 - Generative adversarial networks for whispered to voiced speech conversion: a comparative study JF - International Journal of Speech Technology N2 - Generative Adversarial Networks (GANs) have demonstrated promising results as end-to-end models for whispered to voiced speech conversion. Leveraging non-autoregressive systems like GANs capable of performing conditional waveform generation eliminates the need for separate models to estimate voiced speech features, and leads to faster inference compared to autoregressive methods. This study aims to identify the optimal GAN architecture for the whispered to voiced speech conversion task by comparing six state-of-the-art models. Furthermore, we present a method for evaluating the preservation of speaker identity and local accent, using embeddings obtained from speaker- and language identification systems. Our experimental results show that building the speech conversion system based on the HiFi-GAN architecture yields the best objective evaluation scores, outperforming the baseline by ∼9% relative using frequency-weighted Signal-to-Noise Ratio and Log Likelihood Ratio, as well as by ∼29% relative using Root Mean Squared Error. In subjective tests, HiFi-GAN yielded a mean opinion score of 2.9, significantly outperforming the baseline with a score of 1.4. Furthermore, HiFi-GAN enhanced ASR performance and preserved speaker identity and accent, with correct language detection rates of up to ∼98%. KW - Speech conversion · Generative adversarial networks · Whispered speech · Voiced speech Y1 - 2024 U6 - https://doi.org/10.1007/s10772-024-10161-1 VL - 27 ER - TY - JOUR A1 - Pérez-Toro, Paula Andrea A1 - Vásquez-Correa, Juan Camilo A1 - Bocklet, Tobias A1 - Nöth, Elmar A1 - Orozco-Arroyave, Juan Rafael T1 - User State Modeling Based on the Arousal-Valence Plane: Applications in Customer Satisfaction and Health-Care JF - IEEE Transactions on Affective Computing N2 - The acoustic analysis helps to discriminate emotions according to non-verbal information, while linguistics aims to capture verbal information from written sources. Acoustic and linguistic analyses can be addressed for different applications, where information related to emotions, mood, or affect are involved. The Arousal-Valence plane is commonly used to model emotional states in a multidimensional space. This study proposes a methodology focused on modeling the user’s state based on the Arousal-Valence plane in different scenarios. Acoustic and linguistic information are used as input to feed different deep learning architectures mainly based on convolutional and recurrent neural networks, which are trained to model the Arousal-Valence plane. The proposed approach is used for the evaluation of customer satisfaction in call-centers and for health-care applications in the assessment of depression in Parkinson’s disease and the discrimination of Alzheimer’s disease. F-scores of up to 0.89 are obtained for customer satisfaction, of up to 0.82 for depression in Parkinson’s patients, and of up to 0.80 for Alzheimer’s patients. The proposed approach confirms that there is information embedded in the Arousal-Valence plane that can be used for different purposes. KW - Arousal-valence plane KW - acoustic KW - linguistic KW - customer satisfaction KW - alzheimer’s disease KW - depression Y1 - 2021 U6 - https://doi.org/10.1109/taffc.2021.3112543 SN - 1949-3045 VL - 14 IS - 2 SP - 1533 EP - 1546 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - JOUR A1 - Bayerl, Sebastian P. A1 - Wagner, Dominik A1 - Baumann, Ilja A1 - Bocklet, Tobias A1 - Riedhammer, Korbinian T1 - Detecting Vocal Fatigue with Neural Embeddings JF - Journal of Voice N2 - Vocal fatigue refers to the feeling of tiredness and weakness of voice due to extended utilization. This paper investigates the effectiveness of neural embeddings for the detection of vocal fatigue. We compare x-vectors, ECAPA-TDNN, and wav2vec 2.0 embeddings on a corpus of academic spoken English. Low-dimensional mappings of the data reveal that neural embeddings capture information about the change in vocal characteristics of a speaker during prolonged voice usage. We show that vocal fatigue can be reliably predicted using all three types of neural embeddings after 40 minutes of continuous speaking when temporal smoothing and normalization are applied to the extracted embeddings. We employ support vector machines for classification and achieve accuracy scores of 81% using x-vectors, 85% using ECAPA-TDNN embeddings, and 82% using wav2vec 2.0 embeddings as input features. We obtain an accuracy score of 76%, when the trained system is applied to a different speaker and recording environment without any adaptation. KW - Vocal fatigue KW - Neural embeddings KW - Visualization KW - Detection Y1 - 2023 U6 - https://doi.org/10.1016/j.jvoice.2023.01.012 SN - 0892-1997 PB - Elsevier BV ER - TY - JOUR A1 - Kammerbauer, Roland A1 - Schmitt, Thomas A1 - Bocklet, Tobias T1 - Segmenting Wood Rot using Computer Vision Models N2 - In the woodworking industry, a huge amount of effort has to be invested into the initial quality assessment of the raw material. In this study we present an AI model to detect, quantify and localize defects on wooden logs. This model aims to both automate the quality control process and provide a more consistent and reliable quality assessment. For this purpose a dataset of 1424 sample images of wood logs is created. A total of 5 annotators possessing different levels of expertise is involved in dataset creation. An inter-annotator agreement analysis is conducted to analyze the impact of expertise on the annotation task and to highlight subjective differences in annotator judgement. We explore, train and fine-tune the state-of-the-art InternImage and ONE-PEACE architectures for semantic segmentation. The best model created achieves an average IoU of 0.71, and shows detection and quantification capabilities close to the human annotators. KW - machine learning, image segmentation, semantic segmentation, InternImage, ONEPEACE, lumbering, industrial quality control, industrial automation Y1 - 2024 ER - TY - JOUR A1 - Schmitt, Thomas A1 - Bundscherer, Maximilian A1 - Bocklet, Tobias T1 - Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images N2 - In the food industry, reprocessing returned product is a vital step to increase resource efficiency. [SBB23] presented an AI application that automates the tracking of returned bread buns. We extend their work by creating an expanded dataset comprising 2432 images and a wider range of baked goods. To increase model robustness, we use generative models pix2pix and CycleGAN to create synthetic images. We train state-of-the-art object detection model YOLOv9 and YOLOv8 on our detection task. Our overall best-performing model achieved an average precision AP@0.5 of 90.3% on our test set. KW - machine learning, object detection, YOLOv9, image composition, baked goods, food industry, industrial automation Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2409.20122 ER - TY - JOUR A1 - Freier, Carolin A1 - Bocklet, Tobias A1 - Helten, Anne-Kathrin A1 - Hoffmann, Franziska A1 - Hunger, Marianne A1 - Kovács, László A1 - Richter, Florian A1 - Riedhammer, Korbinian A1 - Schmohl, Tobias A1 - Simon, Claudia T1 - Wie kann videogestütztes Lernen die Erwartungen Studierender und Dozierender erfüllen? JF - Soziale Passagen N2 - ZusammenfassungIm BMBF-Verbundprojekt HAnS entwickeln und implementieren neun Hochschulen sowie drei hochschulübergreifende Einrichtungen ein intelligentes Hochschul-Assistenz-System als Open-Source-Lösung. Videobasierte Lehrmaterialien werden verschriftlicht und durch eine Indexierung Stichwortsuchen ermöglicht; geplant ist, über einen KI-Tutor automatisiert Übungsaufgaben zu generieren. Studierende sollen so in ihrem Selbststudium digital unterstützt werden. Die technische Entwicklung wird interdisziplinär – auch sozialwissenschaftlich und pädagogisch – begleitet und in einem iterativen Vorgehen evidenzbasiert entsprechend Design-Based-Research angepasst. Wissen und Wertesystem der Anwender*innen, Didaktik, Ethik, Akzeptanz und Datenschutz werden dabei im Entwicklungsprozess einbezogen. KW - Hochschulforschung · Intelligente Assistenzsysteme · Evaluation · Video-Lernen · Adressat*innen-zentrierte Technikentwicklung Y1 - 2023 U6 - https://doi.org/10.1007/s12592-023-00478-0 SN - 1867-0180 VL - 15 IS - 2 SP - 631 EP - 635 PB - Springer VS ER - TY - JOUR A1 - Escobar-Grisales, Daniel A1 - Ríos-Urrego, Cristian-David A1 - Baumann, Ilja A1 - Riedhammer, Korbinian A1 - Nöth, Elmar A1 - Bocklet, Tobias A1 - Garcia, Adolfo A1 - Orozco-Arroyave, Juan rafael T1 - It’s Time to Take Action: Acoustic Modeling of Motor Verbs to Detect Parkinson’s Disease N2 - Pre-trained models generate speech representations that are used in different tasks, including the automatic detection of Parkinson’s disease (PD). Although these models can yield high accuracy, their interpretation is still challenging. This paper used a pre-trained Wav2vec 2.0 model to represent speech frames of 25ms length and perform a frame-by-frame discrimination between PD patients and healthy control (HC) subjects. This fine granularity prediction enabled us to identify specific linguistic segments with high discrimination capability. Speech representations of all produced verbs were compared w.r.t. nouns and the first ones yielded higher accuracies. To gaina deeper understanding of this pattern, representations of motor and non-motor verbs were compared and the first ones yielded better results, with accuracies of around 83% in an independent test set. These findings support well-established neurocognitive models about action-related language highlighted as key drivers of PD. Index Terms: computational paralinguistics, interpretability of pre-trained models, action verbs, Parkinson’s disease KW - computational paralinguistics, interpretability of pre-trained models, action verbs, Parkinson’s disease Y1 - 2024 U6 - https://doi.org/10.21437/Interspeech.2024-2205 ER - TY - JOUR A1 - Lee, Seanie A1 - Bok Lee, Dong A1 - Wagner, Dominik A1 - Kang, Minki A1 - Seong, Haebin A1 - Bocklet, Tobias A1 - Lee, Juho A1 - Hwang, Sung Ju T1 - SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models, JF - Findings of the Association for Computational Linguistics: ACL 2025 N2 - Deploying large language models (LLMs) in real-world applications requires robust safety guard models to detect and block harmful user prompts. While large safety guard models achieve strong performance, their computational cost is substantial. To mitigate this, smaller distilled models are used, but they often underperform on “hard” examples where the larger model provides accurate predictions. We observe that many inputs can be reliablyhandled by the smaller model, while only a small fraction require the larger model’s capacity. Motivated by this, we propose SafeRoute, a binary router that distinguishes hard examples from easy ones. Our method selectively applies the larger safety guard model to the data that the router considers hard, improving efficiency while maintaining accuracy compared to solely using the larger safety guard model. Experimental results on multiple benchmark datasets demonstrate that our adaptive model selection significantly enhances the trade-off between computational cost and safety performance, outperforming relevant baselines. Y1 - 2025 UR - https://aclanthology.org/2025.findings-acl.105.pdf PB - Association for Computational Linguistics CY - Vienna, Austria ER - TY - JOUR A1 - Ranzenberger, Thomas A1 - Baumann, Ilja A1 - Bayerl, Sebastian A1 - Wagner, Dominik A1 - Bocklet, Tobias A1 - Riedhammer, Korbinian T1 - Evaluation of recognition errors of hybrid and transformer-based ASR systems in German video lectures JF - Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2025 - Book N2 - We analyze different errors in speech recognition systems, focusing on consecutive insertions and deletions, known as hallucinations and elisions in transformer-based end-to-end automatic speech recognition (ASR) systems. We compare errors from a TDNN-HMM, and whisper-based models on English and German spontaneous speech. Based on a human annotated subset of German lecture videos, we investigate whether these blocks of deletions affect the semantics of the utterance. Whisper performs best and preserves the meaning in 90% of the annotated error segments even containing consecutive deletions on this subset. We analyze the word error rate and do further analysis of errors using natural language processing to detect lemmatization errors, compound word errors, and out-of-vocabulary words. We discuss possible reasons and mitigations. Y1 - 2025 UR - https://www.essv.de/pdf/2025_101_108.pdf PB - ESSV 2025 CY - Halle, Deutschland ER - TY - JOUR A1 - Lippert, Carolyn A1 - Rudolph, Eric A1 - Poltermann, Aleksandra A1 - Engert, Natalie A1 - Lehmann, Robert A1 - Albrecht, Jens T1 - Generative KI in der beraterischen Ausbildung BT - Der Einsatz eines*r virtuellen Klient*in als Übungstool für angehende Onlineberater*innen JF - e-beratungsjournal.net – Zeitschrift für Online-Beratung und computervermittelte Kommunikation N2 - Der Beitrag beleuchtet das Projekt "Virtuelle*r Klient*in (ViKl)" am Institut für E-Beratung der Technischen Hochschule Nürnberg Georg Simon Ohm, das sich mit der Nutzung eines KI-basierten Chatbots für die Simulation von Klient*innen in der Lehrveranstaltung "Querschnitt Onlineberatung" im Bachelor-Studiengang Soziale Arbeit befasst. Ziel ist es, praxisnahe Erfahrungen zu vermitteln und die Fähigkeiten der Studierenden im digitalen Beratungsgespräch zu verbessern. Durch den Einsatz verschiedener maschineller Lernarchitekturen werden passende Antworten generiert, die zum Gesprächsverlauf und zur psychosozialen Problemstellung passen. Die Nutzung einer umfangreichen Datenbasis aus dem Zertifikatskurs "Onlineberatung" sowie ein zielgruppenadäquates didaktisches Konzept tragen zur Qualität des*der ViKl bei. Die Diskussion umfasst auch zukünftige Entwicklungen in der Onlineberatung, die didaktische Gestaltung von KI-basierten Lehr-Lern-Formaten, sowie Herausforderungen und Vorteile dieser Ansätze. KW - Generative KI KW - Hochschule KW - Lehre KW - Computerunterstützte Kommunikation KW - Psychosoziale Beratung KW - Onlinekommunikation KW - digitale Kommunikation KW - Generative KI KW - Onlineberatung KW - Chatberatung Y1 - 2024 U6 - https://doi.org/10.48341/tcgc-st69 SN - 1816-7632 VL - 20 IS - 1 ER -