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Paper Abstract and Keywords
Presentation 2023-01-26 11:05
[Invited Talk] Sound Event Detection and Localization for Humanoid Robots
Yong-Hwa Park (KAIST) EMM2022-64
Abstract (in Japanese) (See Japanese page) 
(in English) Pursuing intelligent machines that recognize human’s condition and surrounding events autonomously, this lecture focuses on sensing and recognition of events in intelligent machine systems by means of sound. This lecture covers recent research outcomes, including environment-robust acoustic event detection and source localization based on the human auditory system knowledge and the state-of-the-art deep learning algorithms as follows:
Targeting robust acoustic event detection against harsh listening condition such as reverberation, background noises, and multiple sources, we focus on sound classification and localization incorporated with specialized acoustic signal featuring, massive data augmentation, and dedicated AI algorithm suitable for non-stationary practical problems, which humanoid robots may easily encounter. For the signal featuring, in-depth functional analysis of human auditory system is carried out via time-frequency analysis to mimic the human organ’s behavior in the sound listening. For the data collection and augmentation, massive environmental sound data were gathered and physics-based data augmentations were carried out on the top of the data collection. Specifically, a dataset of human’s head related transfer function (HRTF) was newly constructed for the application of binaural source localization in humanoid robots. We strongly rely on the acoustic domain knowledge in the design of deep learning scheme considering non-stationary random environment which also the humanoid robot may frequently encounter. The result shows that the dedicated network scheme can outperform other state-of-the-art acoustic recognition neural networks. As outcomes, KAIST cough detection camera, Binaural Sound Event Localization and Detection (BISELD), FilterAugment Scheme, and Temporal Dynamic Convolution Network (TDY-CNN) are demonstrated commonly for the sound event detection and localization of humanoid robots.
Keyword (in Japanese) (See Japanese page) 
(in English) Sound event detection / Sound localization / Head-related transfer function / Humanoid robot / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 368, EMM2022-64, pp. 19-19, Jan. 2023.
Paper # EMM2022-64 
Date of Issue 2023-01-19 (EMM) 
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 EMM2022-64

Conference Information
Committee EMM  
Conference Date 2023-01-26 - 2023-01-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Tohoku Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Sense of Presence, Universal Media, Digital Entertainment, etc. 
Paper Information
Registration To EMM 
Conference Code 2023-01-EMM 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Sound Event Detection and Localization for Humanoid Robots 
Sub Title (in English)  
Keyword(1) Sound event detection  
Keyword(2) Sound localization  
Keyword(3) Head-related transfer function  
Keyword(4) Humanoid robot  
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1st Author's Name Yong-Hwa Park  
1st Author's Affiliation KAIST (KAIST)
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Speaker Author-1 
Date Time 2023-01-26 11:05:00 
Presentation Time 60 minutes 
Registration for EMM 
Paper # EMM2022-64 
Volume (vol) vol.122 
Number (no) no.368 
Page p.19 
#Pages 1 
Date of Issue 2023-01-19 (EMM) 

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