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JRM Vol.29 No.1 pp. 125-136
doi: 10.20965/jrm.2017.p0125
(2017)

Paper:

Audio-Visual Beat Tracking Based on a State-Space Model for a Robot Dancer Performing with a Human Dancer

Misato Ohkita, Yoshiaki Bando, Eita Nakamura, Katsutoshi Itoyama, and Kazuyoshi Yoshii

Graduate School of Informatics, Kyoto University
Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501, Japan

Received:
August 5, 2016
Accepted:
November 30, 2016
Published:
February 20, 2017
Keywords:
robot dancer, real-time beat tracking, state-space model, audio-visual integration
Abstract

Audio-Visual Beat Tracking Based on a State-Space Model for a Robot Dancer Performing with a Human Dancer

An overview of real-time audio-visual beat-tracking for music audio signals and human dance moves

This paper presents a real-time beat-tracking method that integrates audio and visual information in a probabilistic manner to enable a humanoid robot to dance in synchronization with music and human dancers. Most conventional music robots have focused on either music audio signals or movements of human dancers to detect and predict beat times in real time. Since a robot needs to record music audio signals with its own microphones, however, the signals are severely contaminated with loud environmental noise. To solve this problem, we propose a state-space model that encodes a pair of a tempo and a beat time in a state-space and represents how acoustic and visual features are generated from a given state. The acoustic features consist of tempo likelihoods and onset likelihoods obtained from music audio signals and the visual features are tempo likelihoods obtained from dance movements. The current tempo and the next beat time are estimated in an online manner from a history of observed features by using a particle filter. Experimental results show that the proposed multi-modal method using a depth sensor (Kinect) to extract skeleton features outperformed conventional mono-modal methods in terms of beat-tracking accuracy in a noisy and reverberant environment.

References
  1. [1] Y. Sasaki, S. Masunaga, S. Thompson, S. Kagami, and H. Mizoguchi, “Sound localization and separation for mobile robot teleoperation by tri-concentric microphone array,” J. of Robotics and Mechatronics, Vol.19, No.3, pp. 281-289, 2007.
  2. [2] Y. Sasaki, M. Kaneyoshi, S. Kagami, H. Mizoguchi, and T. Enomoto, “Pitch-cluster-map based daily sound recognition for mobile robot audition,” J. of Robotics and Mechatronics, Vol.22, No.3, pp. 402-410, 2010.
  3. [3] Y. Kusuda, “Toyota’s violin-playing robot,” Industrial Robot: An Int. J., Vol.35, No.6, pp. 504-506, 2008.
  4. [4] K. Petersen, J. Solis, and A. Takanishi, “Development of a aural real-time rhythmical and harmonic tracking to enable the musical interaction with the Waseda flutist robot,” Int. Conf. on Intelligent Robots and Systems (IROS), pp. 2303-2308, 2009.
  5. [5] K. Murata, K. Nakadai, R. Takeda, H. G. Okuno, T. Torii, Y. Hasegawa, and H. Tsujino, “A beat-tracking robot for human-robot interaction and its evaluation,” Int. Conf. on Humanoid Robots (Humanoids), pp. 79-84, 2008.
  6. [6] K. Kosuge, T. Takeda, Y. Hirata, M. Endo, M. Nomura, K. Sakai, M. Koizumu, and T. Oconogi, “Partner ballroom dance robot – PBDR –,” SICE J. of Control, Measurement, and System Integration, Vol.1, No.1, pp. 74-80, 2008.
  7. [7] S. Nakaoka, K. Miura, M. Morisawa, F. Kanehiro, K. Kaneko, S. Kajita, and K. Yokoi, “Toward the use of humanoid robots as assemblies of content technologies – realization of a biped humanoid robot allowing content creators to produce various expressions –,” Synthesiology, Vol.4, No.2, pp. 80-91, 2011.
  8. [8] W. T. Chu and S. Y. Tsai, “Rhythm of motion extraction and rhythm-based cross-media alignment for dance videos,” IEEE Trans. on Multimedia, Vol.14, No.1, pp. 129-141, 2012.
  9. [9] T. Shiratori, A. Nakazawa, and K. Ikeuchi, “Rhythmic motion analysis using motion capture and musical information,” Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 89-94, 2003.
  10. [10] T. Itohara, T. Otsuka, T. Mizumoto, T. Ogata, and H. G. Okuno, “Particle-filter based audio-visual beat-tracking for music robot ensemble with human guitarist,” Int. Conf. on Intelligent Robots and Systems (IROS), pp. 118-124, 2011.
  11. [11] D. R. Berman, “AVISARME: Audio visual synchronization algorithm for a robotic musician ensemble,” Master’s thesis, University of Maryland, 2012.
  12. [12] G. Weinberg, A. Raman, and T. Mallikarjuna, “Interactive jamming with shimon: A social robotic musician,” Int. Conf. on Human Robot Interaction (HRI), pp. 233-234, 2009.
  13. [13] K. Petersen, J. Solis, and A. Takanishi, “Development of a real-time instrument tracking system for enabling the musical interaction with the Waseda flutist robot,” Int. Conf. on Intelligent Robots and Systems (IROS), pp. 313-318, 2008.
  14. [14] A. Lim, T. Mizumoto, L. K. Cahier, T. Otsuka, T. Takahashi, K. Komatani, T. Ogata, and H. G. Okuno, “Robot musical accompaniment: Integrating audio and visual cues for real-time synchronization with a human flutist,” Int. Conf. on Intelligent Robots and Systems (IROS), pp. 1964-1969, 2010.
  15. [15] M. Ohkita, Y. Bando, Y. Ikemiya, K. Itoyama, and K. Yoshii, “Audio-visual beat tracking based on a state-space model for a music robot dancing with humans,” Int. Conf. on Intelligent Robots and Systems (IROS), pp. 5555-5560, 2015.
  16. [16] K. Nakadai, H. G. Okuno, and H. Kitano, “Real-time auditory and visual multiple-speaker tracking for human-robot interaction,” J. of Robotics and Mechatronics, Vol.14, No.5, pp. 479-489, 2002.
  17. [17] S. Dixon,“ Evaluation of the audio beat tracking system BeatRoot,” J. of New Music Research, Vol.36, No.1, pp. 39-50, 2007.
  18. [18] M. Goto, “An audio-based real-time beat tracking system for music with or without drum-sounds,” J. of New Music Research, Vol.30, No.2, pp. 159-171, 2001.
  19. [19] A. M. Stark, M. E. P. Davies, and M. D. Plumbley, “Realtime beat-synchronous analysis of musical audio,” Int. Conf. on Digital Audio Effects (DAFx), pp. 299-304, 2009.
  20. [20] D. P. W. Ellis, “ Beat tracking by dynamic programming,” J. of New Music Research, Vol.36, No.1, pp. 51-60, 2007.
  21. [21] M. E. P. Davies and M. D. Plumbley, “Context-dependent beat tracking of musical audio,” IEEE Trans. on Audio, Speech, and Language Processing, Vol.15, No.3, pp. 1009-1020, 2007.
  22. [22] J. L. Oliveira, G. Ince, K. Nakamura, K. Nakadai, H. G. Okuno, L. P. Reis, and F. Gouyon, “Live assessment of beat tracking for robot audition,” Int. Conf. on Intelligent Robots and Systems (IROS), pp. 992-997, 2012.
  23. [23] A. Elowsson, “Beat tracking with a cepstroid invariant neural network,” Int. Society for Music Information Retrieval Conf. (ISMIR), pp. 351-357, 2016.
  24. [24] S. Bddotock, F. Krebs, and G.Widmer, “Joint beat and downbeat tracking with recurrent neural networks,” Int. Society for Music Information Retrieval Conf. (ISMIR), pp. 255-261, 2016.
  25. [25] F. Krebs, S. Bddotock, M. Dorfer, and G. Widmer, “Downbeat tracking using beat synchronous features with recurrent neural networks,” Int. Society for Music Information Retrieval Conf. (ISMIR), pp. 129-135, 2016.
  26. [26] S. Durand and S. Essid, “Downbeat detection with conditional random fields and deep learned features,” Int. Society for Music Information Retrieval Conf. (ISMIR), pp. 386-392, 2016.
  27. [27] C. Guedes, “Extracting musically-relevant rhythmic information from dance movement by applying pitch-tracking techniques to a video signal,” Sound and Music Computing Conf. (SMC), pp. 25-33, 2006.
  28. [28] M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. on Signal Processing, Vol.50, No.2, pp. 174-188, 2002.
  29. [29] K. Nakadai, T. Takahashi, H. G. Okuno, H. Nakajima, Y. Hasegawa, and H. Tsujino, “Design and implementation of robot audition system ‘hark’ – open source software for listening to three simultaneous speakers,” Advanced Robotics, Vol.24, No.5-6, pp. 739-761, 2010.
  30. [30] R. A. Rasch, “Synchronization in performed ensemble music,” Acta Acustica united with Acustica, Vol.43, No.2, pp. 121-131, 1979.
  31. [31] R. Takeda, K. Nakada, K. Komatani, T. Ogata, and H. G. Okuno, “Exploiting known sound source signals to improve ICA-based robot audition in speech separation and recognition,” Int. Conf. on Intelligent Robots and Systems (IROS), pp. 1757-1762, 2007.
  32. [32] S. Maruo, “Automatic chord recognition for recorded music based on beat-position-dependent hidden semi-Markov model,” Master’s thesis, Kyoto University, 2016.

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Last updated on Oct. 16, 2017