FNeural Speech Enhancement with Very Low Algorithmic Latency and Complexity via Integrated full- and sub-band Modeling
Author:
Affiliation:
1. Carnegie Mellon University,Language Technologies Institute,Pittsburgh,USA
2. Università Politecnica Delle Marche,Italy
3. Hyundai Motor Group and 42dot Inc.,Seoul,Korea
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10095700.pdf?arnumber=10095700
Reference46 articles.
1. Multi-Scale Temporal Frequency Convolutional Network with Axial Attention for Multi-Channel Speech Enhancement
2. Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation
3. Resource-Efficient Separation Transformer;subakan,2022
4. Towards Efficient Models for Real-Time Deep Noise Suppression
5. A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement
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1. Decoupled Spatial and Temporal Processing for Resource Efficient Multichannel Speech Enhancement;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
2. Recalling-Enhanced Recurrent Neural Network optimized with Chimp Optimization Algorithm based speech enhancement for hearing aids;Intelligent Decision Technologies;2024-02-20
3. Binaural Multichannel Blind Speaker Separation With a Causal Low-Latency and Low-Complexity Approach;IEEE Open Journal of Signal Processing;2024
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