Multi-channel Target Speech Enhancement using Labeled Random Finite Sets and Deep Learning under Reverberant Environments-Reference-Cited by-同舟云学术

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Multi-channel Target Speech Enhancement using Labeled Random Finite Sets and Deep Learning under Reverberant Environments

Published:2023-10-27 Issue: Volume: Page:640-645
ISSN:
Container-title:2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering (ECICE)
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Author:

Datta Jayanta¹,Firoozabadi Ali Dehghan²,Zabala-Blanco David³,Soria Francisco Ruben Castillo⁴,Adams Martin¹,Perez Claudio¹

Affiliation:

1. University of Chile,Department of Electrical Engineering,Santiago,Chile

2. Universidad Tecnologica Metropolitana,Department of Electricity,Santiago,Chile

3. Universidad Católica del Maule,Department of Computer Science and Industry,Talca,Chile

4. Faculty of Sciences Autonomous University of San Luis Potosi,Telecommunications Engineering,San Luis Potosi,Mexico

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10382605/10382606/10382971.pdf?arnumber=10382971

Reference22 articles.

1. A Real-Time SRP-PHAT Source Location Implementation using Stochastic Region Contraction(SRC) on a Large-Aperture Microphone Array

2. Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates

3. Towards Domain Independence in CNN-based Acoustic Localization using Deep Cross Correlations

4. Robust Sound Source Tracking Using SRP-PHAT and 3D Convolutional Neural Networks

5. Dense-U-Net assisted Localization of Speech Sources in Motion under Reverberant conditions

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