基于动态选择机制的低信噪比单声道语音增强算法

为了提升模型在复杂场景下的信息处理能力,提出了一种基于注意力的动态选择机制,根据当前信息选择性地分配权重,有效融合形变卷积和普通卷积的特征输出,自适应地在卷积形变和标准卷积之间进行权衡,从而提高其表示能力.此外,通过借鉴渐进学习,在不增加额外参数的前提下,通过循环迭代的方式进一步增强了模型的学习能力.在TIMIT公开语料库上使用七种来自NoiseX92的不同噪声,在多种信噪比环境下进行实验,结果表明无论信噪比高低,噪声是否在训练数据集中出现,所提出的算法在可懂度和语音质量等客观评价指标上均优于近期其他的深度学习算法.

Monaural speech enhancement algorithm based on deformable convolution

In order to improve the information processing ability of the model in complex scenes, this paper proposed a dynamic selection mechanism based on attention, which selectively allocated weights according to the current information, effectively fused the feature outputs of deformation convolution and ordinary convolution, and adaptively balanced deformable convolution and standard convolution, so as to improve its representation ability. In addition, the learning ability of the model is further enhanced by means of iteration without additional parameters. It used seven different kinds of noises from Noise-X92 in TIMIT corpus, and carried out experiments in various SNR environments. The results show that the proposed algorithm outperforms other recent deep learning algorithms in terms of intelligibility and speech quality, regardless of SNR and whether noise appears in the training data set.