IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2022-01-21 10:55
A lossless audio codec based on hierarchical residual prediction
Taiyo Mineo, Shouno Hayaru (UEC) IT2021-71 SIP2021-79 RCS2021-239
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we propose a novel lossless audio codec that has precise predictive performance from the neural network and faster decoding speed. The proposed method employs an auxiliary function method to set parameters under the sparse residual constraint. The proposed network structure can be considered as one of the ResNet. We implemented the codec and conducted comparison experiments for state-of-the-art codecs. In the result, we confirmed practical decoding speed and it showed higher compression ability than others except for Monkey's Audio did.
Keyword (in Japanese) (See Japanese page) 
(in English) Lossless Audio Coding / Linear Predictive Coding / Golomb-Rice Coding / Convolutional Neural Network / ResNet / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 328, SIP2021-79, pp. 239-244, Jan. 2022.
Paper # SIP2021-79 
Date of Issue 2022-01-13 (IT, SIP, RCS) 
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 IT2021-71 SIP2021-79 RCS2021-239

Conference Information
Committee RCS SIP IT  
Conference Date 2022-01-20 - 2022-01-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2022-01-RCS-SIP-IT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A lossless audio codec based on hierarchical residual prediction 
Sub Title (in English)  
Keyword(1) Lossless Audio Coding  
Keyword(2) Linear Predictive Coding  
Keyword(3) Golomb-Rice Coding  
Keyword(4) Convolutional Neural Network  
Keyword(5) ResNet  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Taiyo Mineo  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Shouno Hayaru  
2nd Author's Affiliation The University of Electro-Communications (UEC)
3rd Author's Name  
3rd Author's Affiliation ()
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2022-01-21 10:55:00 
Presentation Time 25 minutes 
Registration for SIP 
Paper # IT2021-71, SIP2021-79, RCS2021-239 
Volume (vol) vol.121 
Number (no) no.327(IT), no.328(SIP), no.329(RCS) 
Page pp.239-244 
#Pages
Date of Issue 2022-01-13 (IT, SIP, RCS) 


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan