Beamformed Feature For Learning-Based Dual-Channel Speech Separation

This paper deals with the problem of separating target speech signal from reverberant and noisy environment with dual microphones, where the target speech comes from a predefined direction range. First, we apply two differential beamforming with opposite directions to dual-channel inputs. Then, the power spectra of beamforming outputs are used as input feature of deep learning architecture. As input features, the beamformer outputs reflect not only spectral information but also directional information by their power level difference. And the calculation is very simple. Systematic evaluation and comparison show that the proposed system achieves very good separation performance and substantially outperforms related algorithms under very challenging environments where both interfering speaker, noise and reverberations are present.