Ego-noise reduction using a motor data-guided multichannel dictionary

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Schmidt A, Deleforge A, Kellermann W
Publication year: 2016
Pages range: 1281-1286
ISBN: 978-1-5090-3762-9
ISSN: 2153-0866
Language: English


Abstract

We address the problem of ego-noise reduction, i.e., suppressing the noise a robot causes by its own motions. Such noise degrades the recorded microphone signal massively such that the robot’s auditory capabilities suffer. To suppress it, it is intuitive to use also motor data, since it provides additional information about the robot’s joints and thereby the noise sources. We propose to fuse motor data to a recently proposed multichannel dictionary algorithm for ego-noise reduction. At training, a dictionary is learned that captures spatial and spectral characteristics of ego-noise. At testing, nonlinear classifiers are used to efficiently associate the current robot’s motor state to relevant sets of entries in the learned dictionary. By this, computational load is reduced by one third in typical scenarios while achieving at least the same noise reduction performance. Moreover, we propose to train dictionaries on different microphone array geometries and use them for ego-noise reduction while the head to which the microphones are mounted is moving. In such scenarios, the motor guidedapproach results in significantly better performance values


FAU Authors / FAU Editors

Deleforge, Antoine
Professur für Nachrichtentechnik
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik
Schmidt, Alexander
Professur für Nachrichtentechnik


How to cite

APA:
Schmidt, A., Deleforge, A., & Kellermann, W. (2016). Ego-noise reduction using a motor data-guided multichannel dictionary. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1281-1286). Daejon, KR.

MLA:
Schmidt, Alexander, Antoine Deleforge, and Walter Kellermann. "Ego-noise reduction using a motor data-guided multichannel dictionary." Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejon 2016. 1281-1286.

BibTeX: 

Last updated on 2019-19-04 at 01:10