Deep Neural Networks for Nonlinear Multichannel Speech Enhancement

用于非线性多通道语音增强的深度神经网络

• 批准号: 508337379
• 负责人: Professor Dr.-Ing. Timo Gerkmann
• 金额: --
• 依托单位: Arbeitsbereich Signalverarbeitung (SP)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/508337379?language=en
• 关键词: Deep; Neural; Networks; Nonlinear; Multichannel

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.

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，老龄化社会中越来越多的听力受损的听众以及人机界面都依赖于语音增强算法。这些旨在通过抑制背景噪声和其他不良影响（例如混响）来提高语音质量和清晰度。在多通道设置中，除了利用噪声信号的时间频谱特征之外，算法还可以利用空间信息。传统上，这是通过连接线性空间滤波器（所谓的波束形成器）和可能的非线性和基于机器学习的光谱单通道后滤波器来完成的。相比之下，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，联合空间谱非线性滤波器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，该项目的目标是使用深度神经网络作为灵活且强大的非线性函数逼近器来开发和分析鲁棒的联合空间频谱非线性滤波器。为此，结合了信息论、统计信号处理和机器学习的概念。一旦成功，该项目可能为新型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高度相关性。