The aMOBY project will use results from mathematical harmonic analysis combined with machine learning to acoustically monitor biodiversity. Biodiversity refers to the variety and variability of life on Earth, a diversity that is severely endangered due to human-made threats like habitat destruction, introduction of invasive species, over-population and over-harvesting, and of course climate change resulting from pollution of the atmosphere. Biodiversity monitoring is the repeated observation or measurement of biological diversity to diagnose and quantify its status and changes. A great challenge for monitoring of biodiversity lies in the sheer amount of data which clearly requires a high degree of automation to work on a grand scale.
Learning in high dimensional spaces poses a number of challenges which are referred to as the curse of dimensionality. Music Information Retrieval (MIR), as the interdisciplinary science of retrieving information from music, is very often relying on high dimensional feature representations and models. Hub songs are, according to the music similarity function, similar to very many other songs and as a consequence appear in very many recommendation lists preventing other songs from being recommended at all. It is due to the property of distance concentration which causes all points in a high dimensional data space to be at almost the same distance to each other. This proposed project will explore existing and develop new approaches to deal with these problems by studying their effects on a wide range of methods in MIR, but also multimedia and machine learning. In particular we are planning to (i) study and unify rescaling methods to avoid distance concentration, (ii) explore the role of hubness in unsupervised (clustering, visualization) and supervised learning (classification) in high dimensional spaces.
ATLANTIS attempts to understand and model the very first stages in grounded language learning, as we see in children until the age of three: how pointing or other symbolic gestures emerge from the ontogenetic ritualization of instrumental actions, how words are learned very fast in contextualized language games, and how the first grammatical constructions emerge from concrete sentences. This requires a global, computational theory of symbolic development that informs us about what forces motivate language development, what strategies are exploited in learner and caregiver interactions to come up with more complex compositional meanings, how new grammatical structures and novel interaction patterns and formed, and how the multitude of developmental pathways observed in humans lead to a full system of multi-modal communication skills. This ambitious aim is feasible because there have been very significant advances in humanoid robotics and in the development of sensory-motor competence recently, and the time is ripe to push all this to a higher level of symbolic intelligence, going beyond simple sensory-motor loops or pattern-based intelligence towards grounded semantics, and incremental, long-term, autonomous language learning.
The aim of this project is the development of a computational cognitive model of spatial memory and navigation based on the LIDA (Learning Intelligent Distribution Agent) cognitive architecture, integrated with the other high-level cognitive processes accounted for by LIDA, and physically embodied on a humanoid PR2 robot with the aid of the CRAM (Cognitive Robot Abstract Machine) control system. The LIDA cognitive architecture will be extended by a conceptual and computational, hierarchical spatial memory model, inspired by the neural basis of spatial cognition in brains.
In the project, an approach was developed and implemented to classify chat messages into dialogue acts, focusing on questions and directives (“to-dos”). Our multi-lingual system uses word lexica, a specialized tokenizer and rule-based shallow syntactic analysis to compute relevant features, and then trains statistical models (support vector machines, random forests, etc.) for dialogue act prediction. The classification scores we achieve are very satisfactory on question detection and promising on to-do detection, on English and German data collections.
The goal of this project is to develop technologies for the automatic segmentation and interpretation of audio files and audio streams deriving from different media worlds: music repositories, (Web and terrestrial) radio streams, TV broadcasts, etc. A specific focus is on streams in which music plays an important role.
Interpersonal communication and the coordination and synchronization of actions are fundamental human capacities. People use these functions routinely in activities such as shaking hands, driving a car, playing sports, or playing music as part of an ensemble. To coordinate your actions with someone else’s, you must be able to predict how the other person is going to behave. Music ensemble performance provides a particularly interesting context for studying prediction and coordination because the synchronization between actions must be so precise. Since music is dynamic, or time-varying, ensemble musicians must make predictions about their co-performers’ behaviour as they play, relying primarily on nonverbal cues provided by their co-performers’ body movements, breathing, and sound. This research project investigates the mechanisms underlying musical synchronization in small ensembles, using a combination of perceptual/performance experiments and computational modelling techniques.
Lrn2Cre8 aims to understand the relationship between learning and creativity by means of practical engineering, theoretical study, and cognitive comparison. We begin from the position that creativity is a function of memory, that generates new structures based on memorised ones, by processes which are essentially statistical.
Modern digital multimedia and internet technology have radically changed the ways people find entertainment and discover new interests online, seemingly without any physical or social barriers. Such new access paradigms are in sharp contrast with the traditional means of entertainment. An illustrative example of this is live music concert performances that are largely being attended by dedicated audiences only. The PHENICX project aims at bridging the gap between the online and offline entertainment worlds. It will make use of the state-of-the-art digital multimedia and internet technology to make the traditional concert experiences rich and universally accessible: concerts will become multimodal, multi-perspective and multilayer digital artefacts that can be easily explored, customized, personalized, (re)enjoyed and shared among the users.
Generelles Ziel des Projekts „Automated Coding and Categorizing of Innovation Areas“ (ACCIA) war es, ein intelligentes automatisiertes System zu schaffen, das Belegstellen für problembezogene, innovationsrelevante Äußerungen, welche aus unterschiedlichen Online-Quellen extrahiert wurden, identifizieren, analysieren und kategorisieren kann. Durch die Einbindung von Verfahren aus den Bereichen Textverarbeitung, Document Clustering und Document Classification konnte ein Prozessmodell erarbeitet werden, dass eine bisher rein manuell durchgeführte Innovationsfeldanalyse in ein automatisiertes Modell überführt, in dem die manuelle, expertinnengetriebene Analyse mit automatischen, computerlinguistisch gestützten Verfahren verschränkt wird.