Deep Neural Networks for Nonlinear Multichannel Speech Enhancement

在这个项目中，我们探索如何利用深度神经网络灵活的非线性建模能力来推动多通道语音增强算法的性能超越传统线性波束形成的限制。为了在嘈杂的环境中理解语音，在老龄化社会中，越来越多的听力受损的人类听者以及人机接口依赖于语音增强算法。它们的目的是通过抑制背景噪声和其他不想要的效果(如混响)来提高语音质量和可理解性。在多通道设置中，算法除了利用噪声信号的速度谱特征外，还可以利用空间信息。传统上，这是通过级联线性空间滤波器、所谓的波束形成器和可能的非线性和基于机器学习的频谱单通道后置滤波器来实现的。相反，我们前期工作的统计分析和实验评估表明，如果噪声是非高斯的，联合空间-频谱非线性滤波器的性能可能会优于传统方法。然而，这种分析估计器的参数估计在实践中被证明是困难的。因此，本项目的目标是利用深度神经网络作为灵活而强大的非线性函数逼近器来开发和分析稳健的联合空间-频谱非线性滤波器。为此，将信息论、统计信号处理和机器学习的概念结合在一起。一旦成功，该项目可能为一类新型的非线性多通道语音信号处理方案铺平道路，因此对学术界和工业界都具有很高的实用价值。

In this project, we explore how the flexible nonlinear modelling capacity of deep neural networks can be employed to push the performance of multichannel speech enhancement algorithms beyond the limits imposed by traditional linear beamforming. To understand speech in noisy environments, a growing number of hearing-impaired human listeners in our aging society, as well as human-machine interfaces, rely on speech enhancement algorithms. These aim to improve speech quality and intelligibility by suppressing background noise and other unwanted effects such as reverberation. In a multichannel setting, algorithms can leverage spatial information in addition to exploiting the tempo-spectral characteristics of the noisy signal. Traditionally, this has been done by concatenating a linear spatial filter, a so-called beamformer, and a possibly nonlinear and machine learning-based spectral single-channel postfilter. In contrast, statistical analyses and experimental evaluations of our preliminary work reveal that a joint spatial-spectral nonlinear filter may outperform the traditional approach if the noise is non-Gaussian. However, the estimation of the parameters of such analytical estimators has proven to be difficult in practice. Consequently, this project targets the development and analysis of robust joint spatial-spectral nonlinear filters using deep neural networks as flexible and powerful nonlinear function approximators. For this, concepts from information theory, statistical signal processing, and machine learning are combined. Upon success, this project may pave the way towards a novel class of nonlinear multichannel speech signal processing schemes and is thus of high relevance both for academia and industry.