Spatial-Temporal Graph Convolution Network for Multichannel Speech Enhancement
Author:
Affiliation:
1. Beijing Jiaotong University,Electronic and Information Engineering
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09746054.pdf?arnumber=9746054
Reference22 articles.
1. Single channel speech enhancement using convolutional neural network
2. A Fully Convolutional Neural Network for Speech Enhancement;park;Proc INTERSPEECH,2017
3. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
4. Multi-Channel Speech Enhancement Using Graph Neural Networks
5. Phase-aware speech enhancement with deep complex u-net;choi,2019
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1. Traffic Flow Prediction Algorithm Based on Attention Spatiotemporal Graph Convolution Mechanism;2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC);2024-03-15
2. Classification of Typical Static Objects in Road Scenes Based on LO-Net;Remote Sensing;2024-02-12
3. A Novel Approach to Multi-Channel Speech Enhancement Based on Graph Neural Networks;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024
4. Three-stage hybrid neural beamformer for multi-channel speech enhancement;The Journal of the Acoustical Society of America;2023-06-01
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