Informed Ego-noise Suppression Using Motor Data-driven Dictionaries

The suppression of ego-noise for (humanoid) robots is typically addressed by learning-based techniques. In this paper, we propose a novel approach which models significant parts of ego-noise spectrograms based on motor data and does not require a prior training step. Accordingly, the intrinsic harmonic structure of ego noise is taken into account by introducing a nonnegative matrix factorization (NMF) framework with motor data-driven dictionaries. Limited improvement was observed by employing an additional pre-trained small-sized dictionary accounting for the residual ego-noise. The presented approach exhibits comparable suppression performance to an audio only-based approach trained specifically to the scenario, while the number of dictionary elements which require prior learning can be reduced by a factor of two. For ego-noise resulting from previously unseen movements, the proposed method shows consistently superior suppression results while the audio only-based approach degrades drastically.