Time-Domain Audio Source Separation Based on Gaussian Processes with Deep Kernel Learning-Reference-Cited by-同舟云学术

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Time-Domain Audio Source Separation Based on Gaussian Processes with Deep Kernel Learning

Published:2023-10-22 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
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Author:

Nugraha Aditya Arie¹,Di Carlo Diego¹,Bando Yoshiaki²,Fontaine Mathieu³,Yoshii Kazuyoshi⁴

Affiliation:

1. RIKEN,Center for Advanced Intelligence Project (AIP),Japan

2. National Institute of Advanced Industrial Science and Technology (AIST),Japan

3. Institut Polytechnique de Paris,LTCI, Télécom Paris,France

4. Kyoto University,Graduate School of Informatics,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10248019/10248047/10248168.pdf?arnumber=10248168

Reference39 articles.

1. MossFormer: Pushing the Performance Limit of Monaural Speech Separation Using Gated Single-Head Transformer with Convolution-Augmented Joint Self-Attentions

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

3. Exploring Self-Attention Mechanisms for Speech Separation

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

5. Filter-bank design for end-to-end speech separation;pariente;Proc IEEE ICASSP,2020

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