Deep Learning Based Binaural Speech Separation in Reverberant Environments

作者：Zhang, Xueliang^*; Wang, DeLiang

来源：IEEE/ACM Transactions on Audio Speech and Language Processing, 2017, 25(5): 1075-1084.

DOI：10.1109/TASLP.2017.2687104

• Beamforming
• binaural speech separation
• computational auditory scene analysis (CASA)
• deep neural network (DNN)
• room reverberation

Speech signal is usually degraded by room reverberation and additive noises in real environments. This paper focuses on separating target speech signal in reverberant conditions from binaural inputs. Binaural separation is formulated as a supervised learning problem, and we employ deep learning to map from both spatial and spectral features to a training target. With binaural inputs, we first apply a fixed beamformer and then extract several spectral features. A new spatial feature is proposed and extracted to complement the spectral features. The training target is the recently suggested ideal ratio mask. Systematic evaluations and comparisons show that the proposed system achieves very good separation performance and substantially outperforms related algorithms under challenging multisource and reverberant environments.

• 出版日期2017-5
• 单位内蒙古大学