Making a robot dance to diverse musical genre in noisy environments

Joao Lobato Oliveira, Keisuke Nakamura, Thibault Langlois, Fabien Gouyon, Kazuhiro Nakadai, Angelica Lim, Luis Paulo Reis, Hiroshi G. Okuno
2014 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems  
In this paper we address the problem of musical genre recognition for a dancing robot with embedded microphones capable of distinguishing the genre of a musical piece while moving in a real-world scenario. For this purpose, we assess and compare two state-of-the-art musical genre recognition systems, based on Support Vector Machines and Markov Models, in the context of different real-world acoustic environments. In addition, we compare different preprocessing robot audition variants (single
more » ... nel and separated signal from multiple channels) and test different acoustic models, learned a priori, to tackle multiple noise conditions of increasing complexity in the presence of noises of different natures (e.g., robot motion, speech). The results with six different musical genres suggest improved results, in the order of 43.6pp for the most complex conditions, when recurring to Sound Source Separation and acoustic models trained in similar conditions to the testing scenarios. A robot dance demonstration session confirms the applicability of the proposed integration for genre-adaptive dancing robots in real-world noisy environments.
doi:10.1109/iros.2014.6942812 dblp:conf/iros/OliveiraNLGNLRO14 fatcat:q6uip5cowfdtvdmkuocelop4ry