  Low-complexity speech enhancement on mobile phones is crucial in the era of
5G. Thus, focusing on handheld mobile phone communication scenario, based on
power level difference (PLD) algorithm and lightweight U-Net, we propose
PLD-guided lightweight deep network (PLDNet), an extremely lightweight
dual-microphone speech enhancement method that integrates the guidance of
signal processing algorithm and lightweight attention-augmented U-Net. For the
guidance information, we employ PLD algorithm to pre-process dual-microphone
spectrum, and feed the output into subsequent deep neural network, which
utilizes a lightweight U-Net with our proposed gated convolution augmented
frequency attention (GCAFA) module to extract desired clean speech.
Experimental results demonstrate that our proposed method achieves competitive
performance with recent top-performing models while reducing computational cost
by over 90%, highlighting the potential for low-complexity speech enhancement
on mobile phones.
