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Generative adversarial network-based speech bandwidth extender and extension method

机译:基于生成的对抗网络的语言带宽扩展器和扩展方法

摘要

Proposed are a generative adversarial network-based speech bandwidth extender and extension method. A generative adversarial network-based speech bandwidth extension method, according to an embodiment, comprises the steps of: extracting feature vectors from a narrowband (NB) signal and a wideband (WB) signal of a speech; estimating the feature vector of the wideband signal from the feature vector of the narrowband signal; and learning a deep neural network classification model for discriminating the estimated feature vector of the wideband signal from the actually extracted feature vector of the wideband signal and the actually extracted feature vector of the narrowband signal.
机译:提出是一种基于生成的对抗网络的语言带宽扩展器和扩展方法。 根据一个实施例的基于生成的对抗网络的语言带宽扩展方法包括以下步骤:从窄带(NB)信号和语音的宽带(WB)信号中提取特征向量; 从窄带信号的特征向量估计宽带信号的特征向量; 学习深度神经网络分类模型,用于区分来自宽带信号的实际提取的特征向量的宽带信号的估计特征向量和窄带信号的实际提取的特征向量。

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