Noise correlation matrix estimation for improving sound source localization by multirotor UAV

Koutarou Furukawa, Keita Okutani, Kohei Nagira, Takuma Otsuka, Katsutoshi Itoyama, Kazuhiro Nakadai, Hiroshi G. Okuno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Citations (Scopus)

Abstract

A method has been developed for improving sound source localization (SSL) using a microphone array from an unmanned aerial vehicle with multiple rotors, a 'multirotor UAV'. One of the main problems in SSL from a multirotor UAV is that the ego noise of the rotors on the UAV interferes with the audio observation and degrades the SSL performance. We employ a generalized eigenvalue decomposition-based multiple signal classification (GEVD-MUSIC) algorithm to reduce the effect of ego noise. While GEVD-MUSIC algorithm requires a noise correlation matrix corresponding to the auto-correlation of the multichannel observation of the rotor noise, the noise correlation is nonstationary due to the aerodynamic control of the UAV. Therefore, we need an adaptive estimation method of the noise correlation matrix for a robust SSL using GEVD-MUSIC algorithm. Our method uses a Gaussian process regression to estimate the noise correlation matrix in each time period from the measurements of self-monitoring sensors attached to the UAV such as the pitch-roll-yaw tilt angles, xyz speeds, and motor control values. Experiments compare our method with existing SSL methods in terms of precision and recall rates of SSL. The results demonstrate that our method outperforms existing methods, especially under high signal-to-noise-ratio conditions.

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
Pages3943-3948
Number of pages6
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013 - Tokyo
Duration: 2013 Nov 32013 Nov 8

Other

Other2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013
CityTokyo
Period13/11/313/11/8

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Furukawa, K., Okutani, K., Nagira, K., Otsuka, T., Itoyama, K., Nakadai, K., & Okuno, H. G. (2013). Noise correlation matrix estimation for improving sound source localization by multirotor UAV. In IEEE International Conference on Intelligent Robots and Systems (pp. 3943-3948). [6696920] https://doi.org/10.1109/IROS.2013.6696920