ID 原文 译文
39156 然而，如何从极度非平稳噪声环境下有效地分离出目标语音仍然是一个具有挑战性的问题。 However, how to effectively separate target speech in extremely nonstationary noise environment is still a challenging problem.
39157 基于非负矩阵分解(Nonnegative matrix factorization， NMF)的语音增强算法利用非负的语音和噪声基矩阵来建模语音和噪声的频谱子空间，是目前一种先进的对抑制非平稳噪声非常有效的技术。 Speech enhancement based on nonnegative matrix factorization(NMF) is currently an advanced and effective technique for suppressing nonstationary noise, which models spectral subspaces of speech and noise using nonnegative basis matrices.
39158 本文首先详细地介绍了非负矩阵分解理论，包括非负矩阵分解模型，代价函数(Cost function)的定义以及常用的乘法更新准则(Multiplicative update rules)。 First, in this paper, the theory of nonnegative matrix factorization is introduced in details, including the model of the NMF, the definition of cost functions and the commonly used multiplicative update rules.
39159 然后，本文详细地介绍了基于非负矩阵分解的语音增强方法的基本原理，包括训练阶段和增强阶段的具体过程，并进行了实验， Then, the basic principle of the NMF-based speech enhancement methods is reviewed in details, including the specific processes of the training and enhancement stages, and the experiments are carried out.
39160 此外，还利用一个基于非负矩阵分解的语音重构实验验证了语音基矩阵对语音频谱的建模能力。 In addition, an NMF-based speech reconstruction experiment is used to verify the ability of speech basis matrix for modeling the speech spectrums.
39161 最后，本文总结了传统的基于非负矩阵分解的算法的不足，并对一些现有的基于非负矩阵分解的算法分别做了一个简单的概述，包括其创新点和优缺点，并对比分析了几种具有代表性的方法。 Finally, the shortcomings of the traditional NMF-based algorithms are summarized, and some existing NMF-based algorithms are respectively briefly reviewed including their innovations, advantages and disadvantages. Moreover, several typical methods are analyzed and compared.
39162 本文从历史的角度展示了基于非负矩阵分解的语音增强方法的不断发展。 This paper shows the continuous developments of the NMF-based speech enhancement methods in a historical perspective.
39163 临境语音通信与智能语音交互都面临复杂声学环境中的远距离高保真拾音难题， Immersive communication and human-machine speech interface systems have to face the problem of distant sound acquisition in complex acoustic environments where noise, reverberation, echo, and competing sources may coexist.
39164 解决这一难题的有效途径是使用由多个麦克风传感器组成的麦克风阵列或多通道拾音系统，这种系统的核心是信号处理，通过对空间采样的声场信息进行时、空、频三域的联合处理来实现声源定向/定位、信号增强、噪声抑制、混响抑制、声源分离、声场参数估计等功能。麦克风阵列信号处理的方法有很多，其中研究的最多、使用得最广的方法是波束形成。 To deal with this problem, microphone arrays or more generally multichannel sound acquisition systems have to be used.The major difference between a microphone array and a multichannel system lies in how the microphone sensors are selected and organized.In the former, the microphone sensors are carefully selected to have the same or similar sensitivity, signal-to-noise ratio(SNR), and responses and they are well organized into a particular geometry.All the sensors' signals in such an array are pre-amplified with a same gain and converted to the digital domain with a same clock.In contrast, a multichannel system(either centralized or distributed) may use multiple clocks and have sensors with different characteristics.The central component of both systems are signal processing, which operates on the sensors' outputs to achieve a certain objective such as source localization, noise reduction, signal enhancement, dereverberation, source separation, to name but a few.
39165 本文对麦克风阵列波束形成的原理、进展以及当前常用的方法进行简要综述，内容涵盖延迟求和、超指向、差分、正交级数展开、Kronecker和自适应波束形成方法。 Considering the maturity level as well as the application breadth so far, we focus in this paper on microphone arrays and present a systematic overview of the state of the art of the associated beamforming algorithms including the delay-and-sum, superdirective, differential, and adaptive beamformers.