Deep Residual Echo Suppression and Noise Reduction: A Multi-Input FCRN Approach in a Hybrid Speech Enhancement System
Author:
Affiliation:
1. Technische Universität Braunschweig,Institute for Communications Technology,Braunschweig,Germany,38106
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747386.pdf?arnumber=9747386
Reference30 articles.
1. Towards objective quality assessment of speech enhancement systems in a black box approach
2. Fully Convolutional Recurrent Networks for Speech Enhancement
3. INTERSPEECH 2020 Deep Noise Suppression Challenge: A Fully Convolutional Recurrent Network (FCRN) for Joint Dereverberation and Denoising
4. Deep Noise Suppression with Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data
5. Frequency-domain adaptive Kalman filter for acoustic echo control in hands-free telephones
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1. A Deep Hybrid Model for Stereophonic Acoustic Echo Control;Circuits, Systems, and Signal Processing;2024-08-07
2. A User-Centric Approach for Deep Residual-Echo Suppression in Double-Talk;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024
3. Acoustic Echo Cancellation with the Normalized Sign-Error Least Mean Squares Algorithm and Deep Residual Echo Suppression;Algorithms;2023-03-03
4. A Hybrid Acoustic Echo Reduction Approach Using Kalman Filtering and Informed Source Extraction with Improved Training;2022 IEEE Spoken Language Technology Workshop (SLT);2023-01-09
5. Hybrid-Frequency-Resolution Adaptive Kalman Filter for Online Identification of Long Acoustic Responses With Low Input-Output Latency;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2023
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