Ego noise suppression of a robot using template subtraction

While a robot is moving, the joints inevitably generate noise due to its motors, i.e. ego-motion noise. This problem is very crucial, especially in humanoid robots, because it tends to have a lot of joints and the motors are located closer to the microphones than the sound sources. In this work, we investigate methods for the prediction and suppression of the ego-motion noise. In the first part, we analyze the performance of different noise subtraction strategies, assuming that the noise prediction problem has been solved. In the second part, we present some results for a noise prediction scheme based on the current robot joint status. Performance is evaluated for a number of criteria, including Automatic Speech Recognition (ASR). We demonstrate that our method improves recognition performance during ego-motion considerably.