  Human-robot interaction relies on a noise-robust audio processing module
capable of estimating target speech from audio recordings impacted by
environmental noise, as well as self-induced noise, so-called ego-noise. While
external ambient noise sources vary from environment to environment, ego-noise
is mainly caused by the internal motors and joints of a robot. Ego-noise and
environmental noise reduction are often decoupled, i.e., ego-noise reduction is
performed without considering environmental noise. Recently, a variational
autoencoder (VAE)-based speech model has been combined with a fully adaptive
non-negative matrix factorization (NMF) noise model to recover clean speech
under different environmental noise disturbances. However, its enhancement
performance is limited in adverse acoustic scenarios involving, e.g. ego-noise.
In this paper, we propose a multichannel partially adaptive scheme to jointly
model ego-noise and environmental noise utilizing the VAE-NMF framework, where
we take advantage of spatially and spectrally structured characteristics of
ego-noise by pre-training the ego-noise model, while retaining the ability to
adapt to unknown environmental noise. Experimental results show that our
proposed approach outperforms the methods based on a completely fixed scheme
and a fully adaptive scheme when ego-noise and environmental noise are present
simultaneously.
