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Paper Abstract and Keywords
Presentation 2024-05-22 16:50
[Invited Talk] Fundamentals of Diffusion Models and their Application to Speech Enhancement and Separation
Robin Scheibler (LY Corp.) EA2024-9
Abstract (in Japanese) (See Japanese page) 
(in English) Diffusion models are a class of generative models that operate in an iterative manner, progressively transforming noise into a target distribution. They are behind the recent success of high-quality text-to-image generation models, that well illustratre their representative power. Despite their success, they rely on more intricate mathemical concepts than other generative models, making the barrier to entry in the field higher. In this talk, I will introduce the basics of diffusion models, which come in two, closely related flavors: via Markov chains and via stochastic differential equations. Moving from theory to practice, we will discuss some of the common pitfalls of training diffusion models and the tricks to avoid them. We will cover some of the popular pipelines for image and, recently, audio generation, and text-to-speech. In the second part, we will dive into the application of diffusion models to speech enhancement and separation. We will first motivate the use of generative models for this typically predictive task. Then, I will present some of the popular architectures such as UNIVERSE and score-based generative speech enhancement (SGMSE). After enhancement, we will move to the speech separation task which presents some distinctive challenges and opportunities. The last part will be dedicated to some of the current challenges and future directions
in the field.
Keyword (in Japanese) (See Japanese page) 
(in English) Diffusion models / Generative models / Speech separation / Speech enhancement / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 42, EA2024-9, pp. 38-38, May 2024.
Paper # EA2024-9 
Date of Issue 2024-05-15 (EA) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF EA2024-9

Conference Information
Committee EA  
Conference Date 2024-05-22 - 2024-05-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To EA 
Conference Code 2024-05-EA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fundamentals of Diffusion Models and their Application to Speech Enhancement and Separation 
Sub Title (in English)  
Keyword(1) Diffusion models  
Keyword(2) Generative models  
Keyword(3) Speech separation  
Keyword(4) Speech enhancement  
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1st Author's Name Robin Scheibler  
1st Author's Affiliation LY Corporation (LY Corp.)
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Speaker Author-1 
Date Time 2024-05-22 16:50:00 
Presentation Time 50 minutes 
Registration for EA 
Paper # EA2024-9 
Volume (vol) vol.124 
Number (no) no.42 
Page p.38 
#Pages 1 
Date of Issue 2024-05-15 (EA) 

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