文章基本信息

标题：Motor data-regularized nonnegative matrix factorization for ego-noise suppression
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作者：Alexander Schmidt ; Andreas Brendel ; Thomas Haubner 等
期刊名称：EURASIP Journal on Audio, Speech, and Music Processing
印刷版ISSN：1687-4714
电子版ISSN：1687-4722
出版年度：2020
卷号：2020
期号：1
页码：1
DOI：10.1186/s13636-020-00178-0
出版社：Hindawi Publishing Corporation
摘要：Ego-noise, i.e., the noise a robot causes by its own motions, significantly corrupts the microphone signal and severely impairs the robot’s capability to interact seamlessly with its environment. Therefore, suitable ego-noise suppression techniques are required. For this, it is intuitive to use also motor data collected by proprioceptors mounted to the joints of the robot since it describes the physical state of the robot and provides additional information about the ego-noise sources. In this paper, we use a dictionary-based approach for ego-noise suppression in a semi-supervised manner: first, an ego-noise dictionary is learned and subsequently used to estimate the ego-noise components of a mixture by computing a weighted sum of dictionary entries. The estimation of the weights is very sensitive against other signals beside ego-noise contained in the mixture. For increased robustness, we therefore propose to incorporate knowledge about the physical state of the robot to the estimation of the weights. This is achieved by introducing a motor data-based regularization term to the estimation problem which promotes similar weights for similar physical states. The regularization is derived by representing the motor data as a graph and imprints the intrinsic structure of the motor data space onto the dictionary model. We analyze the proposed method and evaluate its ego-noise suppression performance for a large variety of different movements and demonstrate the superiority of the proposed method compared to an approach without using motor data.
关键词：Ego-noise ; Motor data ; Robot audition ; Humanoïd robot