Complex Neural Spatial Filter: Enhancing Multi-channel Target Speech Separation In Complex Domain

Abstract

To date, mainstream target speech separation (TSS) approaches are formulated to estimate the complex ratio mask (cRM) of the target speech in time-frequency domain under supervised deep learning framework. However, the existing deep models for estimating cRM are designed in the way that the real and imaginary parts of the cRM are separately modeled using real-valued training data pairs. The research motivation of this study is to design a deep model that fully exploits the temporal-spectral-spatial information of multi-channel signals for estimating cRM directly and efficiently in complex domain. As a result, a novel TSS network is designed consisting of two modules, a complex neural spatial filter (cNSF) and an MVDR. Essentially, cNSF is a cRM estimation model and an MVDR module is cascaded to the cNSF module to reduce the nonlinear speech distortions introduced by neural network. Specifically, to fit the cRM target, all input features of cNSF are reformulated into complex-valued repr

Stats

• citations37
• S2 citations—
• github stars0
• HF likes0
• heat score11.85
• arxiv keygu2021complex

Related papers

• Multi-channel Multi-frame ADL-MVDR For Target Speech Separation (2020)0.00
• Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters (2023)10.35
• Temporal-spatial Neural Filter: Direction Informed End-to-end Multi-channel Target Speech Separation (2020)0.00
• Spatialnet: Extensively Learning Spatial Information For Multichannel Joint Speech Separation, Denoising And Dereverberation (2023)13.88
• Multi-scale Feature Fusion Transformer Network For End-to-end Single Channel Speech Separation (2022)0.00
• Tasnet: Time-domain Audio Separation Network For Real-time, Single-channel Speech Separation (2017)20.16
• Two-stage Model And Optimal SI-SNR For Monaural Multi-speaker Speech Separation In Noisy Environment (2020)0.00
• Rtfs-net: Recurrent Time-frequency Modelling For Efficient Audio-visual Speech Separation (2023)0.00