Assessment of general applicability of ego noise estimation: Applications to automatic speech recognition and sound source localization

Noise generated due to the motion of a robot deteriorates the quality of the desired sounds recorded by robot-embedded microphones. On top of that, a moving robot is also vulnerable to its loud fan noise that changes its orientation relative to the moving limbs where the microphones are mounted on. To tackle the non-stationary ego-motion noise and the direction changes of fan noise, we propose an estimation method based on instantaneous prediction of ego noise using parameterized templates. We verify the ego noise suppression capability of the proposed estimation method on a humanoid robot by evaluating it on two important applications in the framework of robot audition: (1) automatic speech recognition and (2) sound source localization. We demonstrate that our method improves recognition and localization performance during both head and arm motions considerably.