Enhanced Neural Beamformer With Spatial Information For Target Speech Extraction

Abstract

Recently, deep learning-based beamforming algorithms have shown promising performance in target speech extraction tasks. However, most systems do not fully utilize spatial information. In this paper, we propose a target speech extraction network that utilizes spatial information to enhance the performance of neural beamformer. To achieve this, we first use the UNet-TCN structure to model input features and improve the estimation accuracy of the speech pre-separation module by avoiding information loss caused by direct dimensionality reduction in other models. Furthermore, we introduce a multi-head cross-attention mechanism that enhances the neural beamformer's perception of spatial information by making full use of the spatial information received by the array. Experimental results demonstrate that our approach, which incorporates a more reasonable target mask estimation network and a spatial information-based cross-attention mechanism into the neural beamformer, effectively improves s

Stats

• citations1
• S2 citations—
• github stars0
• HF likes0
• heat score2.26
• arxiv keyguo2023enhanced

Related papers

• Dual-path Transformer Based Neural Beamformer For Target Speech Extraction (2023)0.00
• Spatial Attention For Far-field Speech Recognition With Deep Beamforming Neural Networks (2019)0.00
• Locate And Beamform: Two-dimensional Locating All-neural Beamformer For Multi-channel Speech Separation (2023)3.58
• Sequential Multi-frame Neural Beamforming For Speech Separation And Enhancement (2019)0.00
• Attention-based Neural Beamforming Layers For Multi-channel Speech Recognition (2021)0.00
• Embedding And Beamforming: All-neural Causal Beamformer For Multichannel Speech Enhancement (2021)13.05
• 3D Neural Beamforming For Multi-channel Speech Separation Against Location Uncertainty (2023)0.00
• Multichannel Loss Function For Supervised Speech Source Separation By Mask-based Beamforming (2019)7.50