Schmidt A, Kellermann W (2021)
Publication Type: Conference contribution
Publication year: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: ICAS 2021 - 2021 IEEE International Conference on Autonomous Systems, Proceedings
Event location: Virtual, Montreal, QC, CAN
ISBN: 9781728172897
DOI: 10.1109/ICAS49788.2021.9551193
The suppression of ego-noise is often addressed using dictionary-based methods where the characteristic spectral structure of ego-noise is approximated by a linear combination of dictionary entries. A blind, entirely audio data-based selection of the dictionary entries is, however, challenging and reacts sensitive against other signals besides ego-noise in a mixture. For a more robust behavior, we propose a motor data-dependent regularization term which promotes similar activations for similar physical states of the robot. The proposed regularization term is added to a multichannel nonnegative matrix factorization (MNMF)-based signal model and according update rules are derived. We analyze the proposed method for a challenging ego-noise scenario and demonstrate the efficacy of the method compared to an approach for which no motor data is used.
APA:
Schmidt, A., & Kellermann, W. (2021). Multichannel nonnegative matrix factorization with motor data-regularized activations for robust ego-noise suppression. In ICAS 2021 - 2021 IEEE International Conference on Autonomous Systems, Proceedings. Virtual, Montreal, QC, CAN: Institute of Electrical and Electronics Engineers Inc..
MLA:
Schmidt, Alexander, and Walter Kellermann. "Multichannel nonnegative matrix factorization with motor data-regularized activations for robust ego-noise suppression." Proceedings of the 2021 IEEE International Conference on Autonomous Systems, ICAS 2021, Virtual, Montreal, QC, CAN Institute of Electrical and Electronics Engineers Inc., 2021.
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