Ego-Noise Suppression for Robots Based on Semi-Blind Infinite Non-Negative Matrix Factorization

This paper addresses ego-motion noise suppres- sion for a robot. Many methods use motion information such as position, velocity and acceleration of each joint to infer ego-motion noise. However, such inference is not reliable since motion information and ego-motion noise are not at all times correlated. We propose a new framework for ego-motion noise suppression based on single channel processing without using any explicit motion information. In the proposed framework, ego-motion noise features are estimated in advance from an ego-motion noise input with Infinite Non-negative Matrix Fac- torization (INMF) which is a non-parametric Bayesian model. After that, the proposed Semi-Blind INMF(SB-INMF) is applied to an input signal consisting of both the target and ego- motion noise signals. The ego-motion noise features which are obtained with INMF are used as input to the SB-INMF and treated as the fixed features to extract the target signal. Finally, the target signal is extracted using newly-estimated features with SB-INMF. The proposed framework was applied to ego-motion noise suppression on two types of humanoid robots. Experimental results showed that ego-motion noise was suppressed well compared to a conventional template-based ego- motion noise suppression method using motion information, and thus it worked properly on a robot which does not have an interface to provide the robot's motion information. Index Terms— robot audition, ego-noise suppression, non- parametric Bayesian