Investigation And Comparison of Optimization Methods for Variational Autoencoder-Based Underdetermined Multichannel Source Separation-Reference-Cited by-同舟云学术

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Investigation And Comparison of Optimization Methods for Variational Autoencoder-Based Underdetermined Multichannel Source Separation

Published:2022-05-23 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Seki Shogo¹,Kameoka Hirokazu¹,Li Li¹

Affiliation:

1. Nippon Telegraph and Telephone Corporation,NTT Communication Science Laboratories,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9745891/9746004/09746980.pdf?arnumber=9746980

Reference40 articles.

1. Fast Multichannel Nonnegative Matrix Factorization With Directivity-Aware Jointly-Diagonalizable Spatial Covariance Matrices for Blind Source Separation

2. An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech

3. Librispeech: An ASR corpus based on public domain audio books

4. Asteroid: The PyTorch-Based Audio Source Separation Toolkit for Researchers

5. Nonnegative Matrix Factorization with the Itakura-Saito Divergence: With Application to Music Analysis

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unsupervised Radio Scene Analysis Using Variational Autoencoder;ICC 2023 - IEEE International Conference on Communications;2023-05-28

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