Fabio Vignoli

A personalized music system for motivation in sport performance

We developed a personalized music system called IM4Sports (interactive music for sports) for individual exercising, although running is the prime target. Research prototype of the system consists of a personal computer, a portable music flash player, a heart sensor strap, and a pedometer.

Flower power

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.

Complex-network theoretic clustering for identifying groups of similar listeners in p2p systems

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.

Enriching music with synchronized lyrics, images and colored lights

We present a method to synchronize popular music with its lyrics at the stanza level. First we apply an algorithm to segment audio content into harmonically similar and/or contrasting progressions, i.e. the stanzas. We map the stanzas found to a sequence of labels, where stanzas with a similar progression are mapped to the same label. The lyrics are analyzed as well to compute a second sequence of labels. Using dynamic programming, an optimal match is found between the two sequences, resulting in a stanza-level synchronization of the lyrics and the audio. The synchronized lyrics can be used to compute a synchronized slide show to accompany the music, where the images are retrieved using the lyrics. For an additional enrichment of the experience, colored light effects are synchronized with the music that are computed from the sets of images. The song segmentation can be done reliably, while the mapping of the audio segments and lyrics gives encouraging results.