Flower Power is an interactive installation that creates immersive light atmospheres by capturing the colors of flowers and re-projecting them in the environment. A camera is suspended over a flower bed and its position depends on the positions of the visitors around the installation, which are detected using presence sensors. The image captured by the camera is rendered into the surrounding environment as a suggestive, saturated light scene. The participants can thus give the power to their favorite flower to set the overall light experience. In this paper we describe the motivation and the realization of the installation, and we discuss insights and reactions collected during the 2010 STRP Festival.

This article presents an approach to automatically create virtual communities of users with similar music preferences in a distributed system. Our goal is to create personalized music channels for these communities using the content shared by its members in peer-to-peer networks for each community. To extract these communities a complex network theoretic approach is chosen. A fully connected graph of users is created using epidemic protocols. We show that the created graph sufficiently converges to a graph created with a centralized algorithm after a small number of protocol iterations. To find suitable techniques for creating user communities, we analyze graphs created from real-world recommender datasets and identify specific properties of these datasets. Based on these properties, different graph-based community-extraction techniques are chosen and evaluated. We select a technique that exploits identified properties to create clusters of music listeners. The suitability of this technique is validated using a music dataset and two large movie datasets. On a graph of 6,040 peers, the selected technique assigns at least 85% of the peers to optimal communities, and obtains a mean classification error of less than 0.05% over the remaining peers that are not assigned to the best community.

This paper assesses the impact of three factors on the music retrieval accuracy of a query-by-melody (QBM) system. The investigated factors are the accuracy of the query provider (the singer), the query transcription accuracy of the acoustic front-end and the length of the query. The music retrieval accuracy is described in terms of a new concept, called the remaining information fraction (RIF). With this new concept it is possible to get more insight in the way the individual factors affect the music retrieval accuracy. The experimental results reported in this paper support the following conclusions: i) the music retrieval accuracy of a QBM system, as assessed on a large set of queries, is strongly correlated with the query transcription accuracy of its front-end, as assessed on a small set of manually transcribed queries, ii) there is still a significant gap between the accuracies obtained with manual and automatically generated query transcriptions, and iii) the music retrieval accuracy starts to drop quickly as soon as the query consists of less than 20 notes

In human communication, speech understanding is greatly improved by the bimodal acoustic-visual effect, with respect to simple speech. This is particularly clear when the communication takes place in noisy environments or for non-native speakers. In this paper, we propose a novel algorithm based on linear approximators that estimates the lip movements from a timed sequence of phonemes. This sequence can be generated from real speech, by a segmentation technique based on a hidden Markov model (HMM), or from a text-to-speech system. The algorithm consists of two modules: the training module and the synthesis module. The training module is based on a eigen-analysis of an audiovisual database recorded for this purpose. The synthesis module takes as input the sequence of phonemes and implements an implicit coarticulation model. A later post-processing step converts the parameters estimated into a sequence of facial animation parameters that are compliant to the new MPEG-4 standard. The algorithm has been tested with FAE (Facial Animation Engine), which is an MPEG-4 compliant system developed at the author's university.