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This paper addresses the challenges and advancements in speech recognition for singing, a domain distinctly different from standard speech recognition. Singing encompasses unique challenges, including extensive pitch variations, diverse vocal styles, and background music interference. We explore key areas such as phoneme recognition, language identification in songs, keyword spotting, and full lyrics transcription. I will describe some of my own experiences when performing research on these tasks just as they were starting to gain traction, but will also show how recent developments in deep learning and large-scale datasets have propelled progress in this field. My goal is to illuminate the complexities of applying speech recognition to singing, evaluate current capabilities, and outline future research directions.
There is high confidence for the hypothesis that in speech perception the cycles of a θ-oscillation segment the auditory signal into syllables [8]. Yet the functionality of the oscillator generating the θ-oscillation is unknown. We follow the finding that, within an auditory scene, speech is perceived as a stream given by temporal coherence [12]. We work with the hypotheses that the θ-oscillator is driven by temporal features providing this coherence. We propose a new temporal feature called O-distance, which detects the onset of a syllable - the starting point to of a θ-cycle–triggered by the temporal distance from to to the instance of the maximal rise of the loudness curve of the vowel. To extract to from the auditory signal, we use the statistical properties of this distance based on the C-center hypothesis [25], which predicts a close temporal relation of the onset consonants to the onset of a vowel. The statistics are derived from reference O-distance extracted from an articulatory database, where the minima and maxima of the loudness are related to maxima and minima of the lower incisor and tongue tip. To judge the quality of the O-distance extracted from the auditory signal, we regard the temporal deviation of the O-distance to the reference O-distance. Currently we achieve a mean deviation of 34ms.
One of the tasks PAULE[1, 2] solves is finding suitable control parameter (cp-)trajectories for a given target acoustic. These cp-trajectories can be used to synthesize speech with the articulatory speech synthesizer of the VocalTractLab (VTL) [3]. If the target acoustic contains substantial microphone noise or other background noises, occasionally PAULE optimizes not for the speech in the target, but for this background noises. By adding a speech/non-speech classifier to the feedback and planning-loop in PAULE this resynthesis of background noises should be mitigated. Unfortunately, the improvements were minor, which might be due to uninformative gradients of the classifier. The importance of informative gradients and the use classifiers to adapt PAULE to different tasks are explained and discussed.
Dual front steering axles are quite common in multi-axled heavy duty trucks. In standard layouts of such axle combinations, the steer motions of the wheels depend not only on the rotation of the steering wheel but also on the movements of the axles. As a consequence, the model complexity of the steering system should match with the complexity of the suspension model. The development of new technologies like advanced driver assistance systems or autonomous driving can only be accomplished efficiently using extensive simulation methods. Such kind of applications demand for computationally efficient vehicle models. This paper presents a steering system model for dual front axles of heavy duty trucks which supplements the suspension model of the axles. The model takes the torsional compliance of the steering column as well as the stiffness of the tie rods and the coupling rod into account. A quasi-static solution provides a straight forward computation including the partial derivatives required for an efficient implicit solver. The steering system model matches perfectly with comparatively lean, but sufficiently accurate multibody suspension models.
Friction has long been an important issue in multibody dynamics. Static friction models apply appropriate regularization techniques to convert the stick inequality and the non-smooth stick-slip transition of Coulomb’s approach into a continuous and smooth function of the sliding velocity. However, a regularized friction force is not able to maintain long-term stick. That is why, dynamic friction models were developed in the last decades. The friction force depends herein not
only on the sliding velocity but also on internal states. The probably best known representative, the LuGre friction model, is based on a fictitious bristle but realizes a too simple approximation. The recently published second order dynamic friction model describes the dynamics of a fictitious bristle more accurately. Its performance is compared here to stick-slip friction models, developed and launched not long ago by commercial multibody software packages.
In an industrial product development process, the Bill of Materials (BOM) is a hierarchical, multi-level representation of all components, parts and quantities of a product. With increasing complexity of industrial products, also BOMs become more complex and thus prone to errors, for example when the individual parts of a product are changed during the product development process. Frequently, these Bill of Materials errors have to be identified manually or by using simple, rule-based schemes. In this paper, we provide a technical background of BOMs, showing the intricacy of temporal BOMs errors in an industrial product development process. The work of other authors, which focused on association mining and tree reconciliation to detect Bill of Materials errors, is analysed. We found that there is currently no system being able to prescribe where in a Bill of Materials and when in the product development process, errors are probable to occur. Also, Machine Learning (ML) methods have not been applied yet. Based on these findings, we formalize the notions Bill of Materials and Bill of Materials errors. Furthermore, we present a deterministic distance measure for BOMS. We provide an answer to the main question of how to represent a Bill of Materials for Machine Learning tasks by solving the orthogonal Procrustes problem for dynamic, hierarchical datasets. Then, we describe an isolation forest based approach to temporal anomaly detection, which points at potential errors in a Bill of Materials at a specific timestamp. Furthermore, we apply Machine Learning and present a multi-output Multi Layer Perceptron for the prediction of temporal Bill of Materials errors. The model predicts where and at which point of time Bill of Materials errors are probable to occur, which renders it a prescriptive system. Eventually, we optimize the performance of our model using contextualization via -means clustering. Finally, we apply our prescriptive pipeline to a real world dataset and show its superiority to existing methods using a qualitative comparison.
Die Anleitung zum selbständigen wissenschaftlichen Arbeiten ist ein fester Bestandteil in der naturwissenschaftlichen Ausbildung, der im letzten Jahr durch Distanz‐Unterricht stark vernachlässigt wurde. Wir stellen hier einen Ansatz vor, bei dem Studierende zu Hause mit Alltagsgegenständen in Teilaufgaben die Aktivität von Hefe in Abhängigkeit von Temperatur und Substrat untersucht haben. Die Ergebnisse des gesamten Kurses wurden in einem Google‐Dokument gesammelt und zentral ausgewertet. Gleichzeitig fand ein Erfahrungsaustausch unter den Lernenden statt und Probleme konnten öffentlich diskutiert werden. Darüber hinaus wurden grundlegende Fähigkeiten wie Beobachten, Protokollieren und detailgetreues Berichten geübt. Die Online‐Dokumentation und ‐Erfassung der Ergebnisse stellt somit eine geeignete Methode dar, die Durchführung von Heimexperimenten zu aktivieren und zu koordinieren.
Dr. Christoph Groneck:
„Erfolgsfaktoren der rezenten Stadtbahnneubauprojekte in Frankreich“. Seit Mitte der 1980er Jahre werden in Frankreich mit großem Erfolg neue oberirdische Straßenbahnsysteme realisiert, nachdem das Verkehrssystem Straßenbahn vormals landesweit nahezu vollständig verschwunden war. Kennzeichnend für die Projekte sind eine meist komplette Neuordnung städtischer Verkehrsstrukturen sowie eine integrierte Betrachtung von Stadtgestaltung und Verkehrsanlagenbau. Der Vortrag zeichnet die Entwicklungsgeschichte der neuen Systeme sowie deren Planungsleitbilder und Erfolgsfaktoren nach.
Jochen Gewecke:
„Rück- und Vorschau Projektkommunikation Regionalstadtbahn Neckar-Alb“. Die RegioStadtbahn Neckar-Alb ist ein großes Verkehrsprojekt in der Region Neckar-Alb mit einem künftigen Schienennetz von 198 Kilometern, davon 38 Kilometer neu oder reaktiviert sowie einem Investitionsvolumen von über 1 Milliarde Euro. Ein erstes Modul geht 2022/23 in Betrieb, während die Innenstadtstrecke durch Tübingen durch einen Bürgerentscheid gestoppt wurde, vielleicht aber nur vorübergehend. Der Vortrag skizziert das Gesamtprojekt und stellt den Wahlkampf zum Bürgerentscheid vor.
Thomas Feig:
„Das Projekt Stadtbahn Regensburg“. Regensburg ist die größte Stadt im süddeutschen Raum, die ihren öffentlichen Personennahverkehr bisher ohne schienengebundenes Verkehrsmittel organisiert. In Beschlüssen des Stadtrates wie dem Leitbild für Energie und Klima, dem aktuellen Stadtentwicklungsplan 2040 und insbesondere mit einem Grundsatzbeschluss aus dem Jahr 2018 ist die Bedeutung des Stadtbahn-Projektes für eine wachsende Stadt und ein nachhaltig ökologisches öffentliches Verkehrssystem dokumentiert. Der Vortrag informiert über die Organisation der Planung, über besondere Herausforderungen und wichtige Meilensteine sowie die erzielten Fortschritte im Projekt.
Frank Steinwede:
„Fahrzeugkonzepte für die Stadtbahn Regensburg“. Die anstehenden Verkehrsaufgaben werden mit ihren besonderen Herausforderungen an das künftige Stadtbahnsystem in Regensburg dargestellt. Diese haben unmittelbare Auswirkungen auf die Rahmenbedingungen, die das künftige Stadtbahnfahrzeug mit seinen Abmessungen zu erfüllen hat. Anhand verschiedener Fahrzeugkonzepte wird der Weg zum Wunschfahrzeugtyp hergeleitet.
Sandra Schönherr:
„Netzkonzeption Busverkehre im Rahmen des Projekts Stadtbahn Regensburg“. Regensburg steht vor der Herausforderung, den Ansprüchen einer wachsenden Stadt und der nicht zuletzt durch die Entwicklungen im Energiesektor beeinflusste Mobilitätswende mit einem attraktiven und zukunftsfähigen Mobilitätsangebot gerecht zu werden. Die Entscheidung für ein höherwertiges ÖPNV-System - die Stadtbahn - wurde 2018 getroffen. Das Stadtwerk Regensburg.Mobilität GmbH (SMO) mit der Aufgabe betraut, ein funktionsfähiges ÖPNV-Netz zu konzipieren, das die Stadtbahn durch optimierten Stadtbuseinsatz unter den gegebenen infrastrukturellen Rahmenbedingungen ergänzt. Der Vortrag wird die Strategie vorgestellt, mit der die SMO dieses Ziel erreichen wird.
A cable-driven parallel robot (CDPR) was developed for the installation of curtain wall modules (CWM). The research addressed the question of whether the CDPR was capable installing CWMs with sufficient accuracy while being competitive compared to conventional manual methods. In order to develop and test such a system, a conceptual framework that consisted of three sub-systems was defined. The tests, carried out in two close-to-real demonstration buildings, revealed an absolute accuracy of the CWM installation of 4 to 23 mm. The working time for installing a CWM was reduced to 0.51 h. The results also show that the system is competitive for a workspace greater than 96 m2 compared to conventional manual methods. However, improvements such as reducing the hours for setting up the CDPR on the one hand and achieving a faster and more robust MEE on the other hand will be still necessary in the future.
In recent years, there has been an increase in robots that perform activities in the built environment. In this chapter, projects were featured as case studies to demonstrate the potential of using automation and robotic technologies to facilitate building façade installation tasks. The Building Energy Renovation Through Timber-Prefabricated Modules (BERTIM) and the Highly automatEd PHysical Achievements and performancES using cable roboTs Unique Systems (HEPHAESTUS) research projects consist of researching, developing and prototyping different automated solutions for the handling of building envelopes in various processes of the installation sequence. The BERTIM research project focuses on building envelope upgrading, automated building façade manufacturing, while the HEPHAESTUS research project is focused on the cable-driven parallel robot for the installation of curtain wall modules. Both projects have proved the potential use of new technologies for the installation of prefabricated envelopes and reduced working time and costs during installation.