MSHP-Net: A lightweight multi-scale speech separation model for single-channel signals

Deep neural networks have demonstrated remarkable performance in speech separation tasks. However, the high computational complexity and parameter count of current speech separation models limit their practical deployment in real-world scenarios. Achieving high separation performance while maintaining low model complexity remains a significant challenge. To address this challenge, we propose a novel speech separation method named MSHP-Net (Multi-Scale Hierarchical Perceptual Network). Built upon the powerful SepReformer architecture, MSHP-Net employs a multi-scale hierarchical perceptual encoder to efficiently extract audio features, and leverages efficient Transformer modules to collaboratively complement contextual dependencies. Experimental results on two public datasets, Libri2Mix and LRS2-2Mix, demonstrate that MSHP-Net achieves the best separation performance among the compared lightweight models on Libri2Mix: at a parameter count of 1.26M and a computational cost of 8.59 G/s, it improves SI-SNRi by about 0.9 dB over TIGER-small, and its larger configuration, MSHP-Net Large, attains the best accuracy among them on LRS2-2Mix.