一种基于时域全面注意力机制的单通道语音分离模型OA

Single-channel speech separation aims to extract clean target speaker speech from a mixed audio signal recorded by a single microphone,with significant application value in scenarios such as smart homes,conference systems,and hearing aids.With the rapid development of deep learning technology,self-attention network-based approaches to single-channel speech separation have achieved remarkable progress.While self-attention networks excel at capturing contextual information in long sequence,they still exhibit limitations in capturing detailed fea-tures such as temporal/spectral continuity,spectral structure,and timbre in real-world speech scenarios.Moreover,existing separation architectures based on a single attention paradigm struggle to achieve effective multi-scale fea-ture fusion.To address these challenges,this paper proposed a Temporal Comprehensive Attention Network(TCANet),which addresses the aforementioned issues through a synergistic design of local and global attention modules.Local modeling employs an S&C-SENet-enhanced Conformer structure to capture short-term features such as spectral structure and timbre in detail,while global modeling incorporates a modified Transformer module with relative position embedding to explicitly learn long-term speech dependencies in speech.Furthermore,TCANet achieves cross-scale fusion of intra-block local features and inter-block global correlations through a dimension transformation mechanism.Experimental results on three benchmark datasets—LRS2-2Mix,Libri2Mix,and EchoSet—demonstrate that the proposed method outperforms existing end-to-end speech separation approaches in terms of scale-invariant signal-to-noise ratio improvement(SI-SNRi)and signal-to-distortion ratio improvement(SDRi).