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EvoEvo Deliverable 5.2: Impact obtained from EvoEvo mechanisms on evolution of a hardware personal companion

Guillaume Beslon 1, 2 Jonas Abernot 1 Sergio Peignier 2, 1 Christophe Rigotti 3, 2, 1 
1 BEAGLE - Artificial Evolution and Computational Biology
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This report presents the EvoMove musical companion that generates music according to performer moves. It uses wireless sensors (accelerometers, gyroscopes and magnetometers) to acquire a continuous stream of information about the motions of the performer. This stream is analyzed on-the-fly to exhibit categories of similar moves and to identify the category of new incoming moves. The main feature is that these categories are not predefined but are determined in a dynamic way by a new subspace clustering algorithm. This algorithm, called SubCMedians, stems from our previous work on the Chameleoclust+ algorithm (Deliver-able 5.1) and is targeted towards more efficient processing. SubCMedians is a median-based subspace clustering algorithm using a weight-based hill climbing strategy and a stochastic local exploration step. It is shown to exhibit satisfactory quality clusters when compared to well-established clustering paradigms, while allowing for fast on-the-fly handling of the sensor data. The music generation itself relies on a tiling over time of audio samples, where each sample is triggered according to the moves detected in the data stream coming from the sensors.
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Submitted on : Friday, August 25, 2017 - 12:43:03 AM
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Guillaume Beslon, Jonas Abernot, Sergio Peignier, Christophe Rigotti. EvoEvo Deliverable 5.2: Impact obtained from EvoEvo mechanisms on evolution of a hardware personal companion. [Research Report] INRIA Grenoble - Rhône-Alpes. 2016. ⟨hal-01577180⟩



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