A. Schmidt (FAU Erlangen-Nuremberg), A. Deleforge (INRIA Rennes), and W. Kellermann (FAU Erlangen-Nuremberg)
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Daejeon, South Korea, Oct. 9-14, 2016
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 guided approach results in significantly better performance values.
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