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Content Provider | IEEE Xplore Digital Library |
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Author | Sarabia, M. Kyuhwa Lee Demiris, Y. |
Copyright Year | 2015 |
Description | Author affiliation: Non-invasive Brain-machine Interface Lab., Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland (Kyuhwa Lee) || Dept. of Electr. & Electron. Eng., Personal Robot. Lab., London, UK (Sarabia, M.; Demiris, Y.) |
Abstract | We present an adaptive musical collaboration framework for interaction between a human and a robot. The aim of our work is to develop a system that receives feedback from the user in real time and learns the music progression style of the user over time. To tackle this problem, we represent a song as a hierarchically structured sequence of music primitives. By exploiting the sequential constraints of these primitives inferred from the structural information combined with user feedback, we show that a robot can play music in accordance with the user's anticipated actions. We use Stochastic Context-Free Grammars augmented with the knowledge of the learnt user's preferences. We provide synthetic experiments as well as a pilot study with a Baxter robot and a tangible music table. The synthetic results show the synchronisation and adaptivity features of our framework and the pilot study suggest these are applicable to create an effective musical collaboration experience. |
Starting Page | 715 |
Ending Page | 721 |
File Size | 1311366 |
Page Count | 7 |
File Format | |
e-ISBN | 9781467367042 |
DOI | 10.1109/ROMAN.2015.7333649 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-08-31 |
Publisher Place | Japan |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Robots Grammar Probability distribution Collaboration Synchronization Real-time systems Prediction algorithms |
Content Type | Text |
Resource Type | Article |
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