Making a robot dance to diverse musical genre in noisy environments

João Lobato Oliveira, Keisuke Nakamura, Thibault Langlois, Fabien Gouyon, Kazuhiro Nakadai, Angelica Lim, Luis Paulo Reis, Hiroshi G. Okuno

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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 channel 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.

本文言語English
ホスト出版物のタイトルIEEE International Conference on Intelligent Robots and Systems
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1896-1901
ページ数6
ISBN(印刷版)9781479969340
DOI
出版ステータスPublished - 2014 10月 31
外部発表はい
イベント2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago
継続期間: 2014 9月 142014 9月 18

Other

Other2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
CityChicago
Period14/9/1414/9/18

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用

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