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Speech enhancement method based on multi-band excitation model

机译：基于多频带激励模型的语音增强方法

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摘要

In this paper, we propose a novel speech enhancement method using multi-band excitation (MBE) model. MBE model is a famous and efficient way of speech coding. Motivated by high quality of its synthetic speech, we introduce the MBE model to single-channel speech enhancement system. In the MBE model, the entire frequency band is divided into several sub-bands and each sub-band is formed as voiced or unvoiced speech. In order to reconstruct speech, there are three acoustic parameters of the MBE model need to be estimated, including pitch, harmonic magnitude and voiced/unvoiced (V/UV) decision for each band. To calculate the parameters accurately, deep neural networks (DNNs) are utilized to estimate harmonic magnitude and V/UV decision. In order to learn the mapping relationship of the features deeply, different types of noise and different input signal to noise ratios (SNRs) of noisy speech are combined to form a big training set. Another parameter, pitch, is calculated from the pre-processed speech using MBE analysis method. Moreover, speech presence probability is introduced in this paper to remove residual noise further. Experimental results show that the proposed method can provide higher speech quality and intelligibility compared with some reference methods to some extent. (C) 2020 Elsevier Ltd. All rights reserved.
机译：在本文中，我们提出了一种使用多频带激励（MBE）模型的新型语音增强方法。 MBE模型是一种著名且有效的语音编码方式。由于其高质量的合成语音，我们将MBE模型引入单通道语音增强系统。在MBE模型中，整个频带分为几个子带，每个子带都形成有声或无声语音。为了重建语音，需要估计MBE模型的三个声学参数，包括每个频段的音高，谐波幅度和浊音/清音（V / UV）决策。为了精确地计算参数，利用深度神经网络（DNN）估计谐波幅度和V / UV决策。为了深入学习特征的映射关系，将不同类型的噪声和不同的噪声语音输入信噪比（SNR）组合在一起，形成一个较大的训练集。使用MBE分析方法从预处理后的语音中计算出另一个参数，音调。此外，本文介绍了语音存在概率，以进一步消除残留噪声。实验结果表明，与某些参考方法相比，该方法可以提供更高的语音质量和清晰度。 （C）2020 Elsevier Ltd.保留所有权利。

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