Multi-channel End-to-end Neural Network For Speech Enhancement, Source Localization, And Voice Activity Detection

Abstract

Speech enhancement and source localization has been active research for several decades with a wide range of real-world applications. Recently, the Deep Complex Convolution Recurrent network (DCCRN) has yielded impressive enhancement performance for single-channel systems. In this study, a neural beamformer consisting of a beamformer and a novel multi-channel DCCRN is proposed for speech enhancement and source localization. Complex-valued filters estimated by the multi-channel DCCRN serve as the weights of beamformer. In addition, a one-stage learning-based procedure is employed for speech enhancement and source localization. The proposed network composed of the multi-channel DCCRN and the auxiliary network models the sound field, while minimizing the distortionless response loss function. Simulation results show that the proposed neural beamformer is effective in enhancing speech signals, with speech quality well preserved. The proposed neural beamformer also provides source localizat

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keychen2022multi

Related papers

• Multichannel Speech Enhancement Without Beamforming (2021)9.41
• DCCRN: Deep Complex Convolution Recurrent Network For Phase-aware Speech Enhancement (2020)20.78
• DCCRN+: Channel-wise Subband DCCRN With SNR Estimation For Speech Enhancement (2021)0.00
• Spatial-dccrn: Dccrn Equipped With Frame-level Angle Feature And Hybrid Filtering For Multi-channel Speech Enhancement (2022)5.84
• Embedding And Beamforming: All-neural Causal Beamformer For Multichannel Speech Enhancement (2021)13.05
• Multichannel Loss Function For Supervised Speech Source Separation By Mask-based Beamforming (2019)7.50
• Deep Beamforming For Speech Enhancement And Speaker Localization With An Array Response-aware Loss Function (2023)0.00
• A Unified Multichannel Far-field Speech Recognition System: Combining Neural Beamforming With Attention Based End-to-end Model (2024)0.00