Joint Reduction of Ego-noise and Environmental Noise with a Partially-adaptive Dictionary

Abstract:
We consider the problem of simultaneous reduction of egonoise, i.e., the noise produced by a robot, and environmental noise. Both noise types may occur simultaneously for humanoid interactive robots. Dictionary- and template-based approaches have been proposed for ego-noise reduction. However, most of them lack adaptability to unseen noise types and thus exhibit limited performance in real-world scenarios with environmental noise. Recently, a variational autoencoder (VAE)-based speech model combined with a fully-adaptive dictionary-based noise model, i.e., non-negative matrix factorization (NMF), has been proposed for environmental noise reduction, showing decent adaptability to unseen noise data. In this paper, we propose to extend this framework with a partially-adaptive dictionary-based noise model, which partly adapts to unseen environmental noise while keeping the part pre-trained on ego-noise unchanged. With appropriate sizes, we demonstrate that the partially-adaptive approach outperforms the approaches based on the fully-adaptive and completely-fixed dictionaries, respectively.