  Target speaker extraction (TSE) aims to isolate a specific voice from
multiple mixed speakers relying on a registerd sample. Since voiceprint
features usually vary greatly, current end-to-end neural networks require large
model parameters which are computational intensive and impractical for
real-time applications, espetially on resource-constrained platforms. In this
paper, we address the TSE task using microphone array and introduce a novel
three-stage solution that systematically decouples the process: First, a neural
network is trained to estimate the direction of the target speaker. Second,
with the direction determined, the Generalized Sidelobe Canceller (GSC) is used
to extract the target speech. Third, an Inplace Convolutional Recurrent Neural
Network (ICRN) acts as a denoising post-processor, refining the GSC output to
yield the final separated speech. Our approach delivers superior performance
while drastically reducing computational load, setting a new standard for
efficient real-time target speaker extraction.
