Assessment of single-channel ego noise estimation methods

While a robot is moving, ego noise is generated due to the fans and motors of the robot. Furthermore, a robot is not only subject to the ego noise, but also to the ambient noise of the environment, both having different short-term signal characteristics. Because ego-motion noise generated by the motors is non-stationary, and the BackGround Noise (BGN) is stationary, one single noise estimation method is unable to track the changes in both noise spectra rapidly and accurately. Therefore, we propose to use the combination of two different noise estimation methods adequate for each one of co-existing noise types in a unified framework: 1) a stationary noise estimation method called Histogram-based Recursive Level Estimation (HRLE) and 2) a non-stationary noise estimation method called Template-based Estimation (TE). In this paper, we evaluate the performance of several single-channel based noise estimation techniques in terms of their prediction accuracy and quality of the speech signals enhanced by spectral subtraction methods. The experimental results show that our system, compared to the conventional single-stage noise estimation methods, achieves better performance in attaining signal quality and improving word correct rates.