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.

翻译：人机交互依赖于能够从受环境噪声及机器人自身运动噪声（即自噪声）影响的音频记录中估计目标语音的鲁棒音频处理模块。外部环境噪声源随环境变化，而自噪声主要由机器人内部电机和关节产生。自噪声与环境噪声的降噪通常被解耦处理，即自噪声抑制不考虑环境噪声。近年来，基于变分自编码器（VAE）的语音模型与全自适应非负矩阵分解（NMF）噪声模型相结合，以恢复不同环境噪声干扰下的纯净语音。然而，其在涉及自噪声等不利声学场景下的增强性能有限。本文提出一种多通道部分自适应方案，利用VAE-NMF框架联合建模自噪声与环境噪声：通过预训练自噪声模型利用其空间与频谱结构化特征，同时保留对未知环境噪声的自适应能力。实验结果表明，当自噪声与环境噪声同时存在时，所提方法优于完全固定方案与全自适应方案。