Enhancing End-to-End Multi-Channel Speech Separation Via Spatial Feature Learning
Enhancing End-to-End Multi-Channel Speech Separation Via Spatial Feature Learning
Hand-crafted spatial features (e.g., inter-channel phase difference, IPD) play a fundamental role in recent deep learning based multi-channel speech separation (MCSS) methods. However, these manually designed spatial features are hard to incorporate into the end-to-end optimized MCSS framework. In this work, we propose an integrated architecture for learning spatial features …