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Multi-channel speech enhancement seeks to utilize spatial information todistinguish target speech from interfering signals. While deep learningapproaches like the dual-path convolutional recurrent network (DPCRN) have madestrides, challenges persist in effectively modeling inter-channel correlationsand amalgamating multi-level information. In response, we introduce theParallel Dual-Path Convolutional Recurrent Network (PDPCRN). This acousticmodeling architecture has two key innovations. First, a parallel design withseparate branches extracts complementary features. Second, bi-directionalmodules enable cross-branch communication. Together, these facilitate diverserepresentation fusion and enhanced modeling. Experimental validation on TIMITdatasets underscores the prowess of PDPCRN. Notably, against baseline modelslike the standard DPCRN, PDPCRN not only outperforms in PESQ and STOI metricsbut also boasts a leaner computational footprint with reduced parameters.

PDPCRN: Parallel Dual-Path CRN with Bi-directional Inter-Branch Interactions for Multi-Channel Speech Enhancement