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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Simultaneous Optimization of Forgetting Factor and Time-frequency Mask for Block Online Multi-channel Speech Enhancement
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Simultaneous Optimization of Forgetting Factor and Time-frequency Mask for Block Online Multi-channel Speech Enhancement

机译：在线多通道语音增强的遗忘因子和时频掩码的同时优化

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摘要

In this paper, we propose a block-online multi-channel speech enhancement technique which simultaneously optimizes time-frequency masks and forgetting factors for estimation of multichannel covariance matrices of the desired speech signal and the noise signal so as to maximize speech enhancement performance under the condition that environmental changes occur. The proposed method reduces the noise signal by using a multi-channel Wiener filter (MWF) which is generated by the covariance matrices with the estimated forgetting factors and the estimated time-frequency masks which are outputs of the proposed neural network. The proposed method learns all the parameters of the proposed neural network so as to maximize the speech enhancement performance. Three types of the input features for the forgetting factors adaptation are proposed. The first one is the magnitude spectral of the microphone input signal. The second one is the MWF output with the previous-block filter that is adapted in the previous block. The third one is the inner product between the microphone input signal and the estimated covariance matrices in the previous block. Experimental results show that the proposed method can reduce noise signal more accurately than the conventional equally weight sample averaging.
机译：在本文中，我们提出了一种块在线多通道语音增强技术，该技术可同时优化时频掩码和遗忘因子，以估计所需语音信号和噪声信号的多通道协方差矩阵，从而最大程度地提高语音增强性能。环境发生变化的条件。所提出的方法通过使用由协方差矩阵生成的多通道维纳滤波器（MWF）来降低噪声信号，该协方差矩阵具有所估计的遗忘因子和所估计的时频掩码，这是所提出的神经网络的输出。所提出的方法学习所提出的神经网络的所有参数，从而最大化语音增强性能。提出了三种用于遗忘因子自适应的输入特征。第一个是麦克风输入信号的幅度谱。第二个是MWF输出，其中MWF输出带有在前一个块中适配的前一个块滤波器。第三个是麦克风输入信号与前一个块中估计的协方差矩阵之间的内积。实验结果表明，与传统的等权样本平均相比，该方法可以更准确地降低噪声信号。

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