Exploring Self-Attention Mechanisms for Speech Separation
Author:
Affiliation:
1. Université Laval, Quebec, QC, Canada
2. Concordia University, Montreal, QC, Canada
3. Universitá Politecnica delle Marche, Castelfidardo, Italy
4. Université de Sherbrooke, Sherbrooke, QC, Canada
5. Mila-Quebec AI Institute, Montreal, QC, Canada
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics
Link
http://xplorestaging.ieee.org/ielx7/6570655/9970249/10144582.pdf?arnumber=10144582
Reference53 articles.
1. Reformer: The efficient transformer;kitaev;Proc Int Conf Learn Representations,0
2. Linformer: Self-attention with linear complexity;wang,2020
3. LibriMix: An open-source dataset for generalizable speech separation;cosentino,2020
4. Attention Is All You Need In Speech Separation
5. PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network
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