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Human-robot interaction relies on a noise-robust audio processing modulecapable of estimating target speech from audio recordings impacted byenvironmental noise, as well as self-induced noise, so-called ego-noise. Whileexternal ambient noise sources vary from environment to environment, ego-noiseis mainly caused by the internal motors and joints of a robot. Ego-noise andenvironmental noise reduction are often decoupled, i.e., ego-noise reduction isperformed without considering environmental noise. Recently, a variationalautoencoder (VAE)-based speech model has been combined with a fully adaptivenon-negative matrix factorization (NMF) noise model to recover clean speechunder different environmental noise disturbances. However, its enhancementperformance is limited in adverse acoustic scenarios involving, e.g. ego-noise.In this paper, we propose a multichannel partially adaptive scheme to jointlymodel ego-noise and environmental noise utilizing the VAE-NMF framework, wherewe take advantage of spatially and spectrally structured characteristics ofego-noise by pre-training the ego-noise model, while retaining the ability toadapt to unknown environmental noise. Experimental results show that ourproposed approach outperforms the methods based on a completely fixed schemeand a fully adaptive scheme when ego-noise and environmental noise are presentsimultaneously.

Partially Adaptive Multichannel Joint Reduction of Ego-noise and Environmental Noise