Att-U-Net:融合注意力机制的U-Net骨导语声增强

Att-U-Net:bone conducted speech enhancementbased on U-Net with attention mechanism

近年来,大量全卷积网络、U-Net等编解码网络结构应用于语声增强,然而,此类结构不能充分利用先后时间与高低频率之间的关联信息,对于处理长序列数据存在信息丢失的问题。为保持计算效率的同时实现更充分的时频关联信息建模,该文提出一种融合注意力机制的U-Net网络的骨导语声增强方法(Att-U-Net),通过在跳跃连接中引入注意力机制,生成一个权重矩阵,将编码层中的全局信息根据权重融入对应的解码层中,使网络在编解码过程中能够关注输入数据中与增强目标相关程度高的重要信息,同时抑制不相关的信息。在骨导语声数据集上的实验表明,融合注意力机制的U-Net网络能在保持模型轻量化的同时有效提升骨导语声的增强效果,增强后的语声在各项客观评价指标上均优于基线模型。

In recent years,a large number of decoded networks are applied to speech enhancement,such as full convolutional networks,U-Net,etc.However,Encoder-Decoder structures can not make the best of the correlation information on time series and relationship between high and low frequencies and have the problem of information loss for the long sequence input data.In order to maintain the computational efficiency and realize more sufficient time-frequency correlation information modeling,this paper proposes a bone conducted speech enhancement method(Att-U-Net),which combines U-Net network and attention mechanism.Through introducing attention mechanism into skip connection and generating a weight matrix,the global information in the encoding layer is transmitted to the corresponding decoding layer according to the weight coefficient in the process of encoding and decoding.The network can pay attention to the important information highly related to the enhancement target in the input data,while suppressing the irrelevant information.Experiments on bone conduction speech dataset show that the U-Net integrating attention mechanism can effectively improve the bone conduction speech enhancement effect while maintaining the lightweight of the model.The enhanced speech is better than the baseline model in objective evaluation indicators.