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| Presentation | 2017-03-01 12:40 [Poster Presentation] Indoor-environmental sound identification based on deep neural network with higher-dimensional features Sakiko Mishima, Yukoh Wakabayashi, Takahiro Fukumori, Masato Nakayama, Takanobu Nishiura (Ritsumeikan Univ.)  EA2016-87 SIP2016-142 SP2016-82 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Surveillance systems with a video camera have been utilized for the safety of people. It is important to identify the indoor-environmental sound in order to monitor the situations in the dark and blind areas. In the past, the acoustic model has been constructed on the basis of hidden Markov model (HMM) with mel frequency cepstrum coefficient (MFCC). However, it is difficult to identify the indoor-environmental sound with high accuracy because the acoustic features of the sound are effected by the reverberation. We propose the method to identify the indoor-environmental sound on the basis of deep neural network (DNN) with higher-dimentional features. In this paper, we investigate filter bank features, log-power spectrum and waveform as higher-dimensional features.From an evaluation experiment, we confirm the effectiveness of the proposed method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Environmental sound discrimination / Higher-dimentional feature / Deep neural network / Acoustic model / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 475, EA2016-87, pp. 31-36, March 2017. | |
| Paper # | EA2016-87 | |
| Date of Issue | 2017-02-22 (EA, SIP, SP) | |
| ISSN | Print edition: ISSN 0913-5685 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 | EA2016-87 SIP2016-142 SP2016-82 |
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| Conference Information | |
| Committee | SP SIP EA |
| Conference Date | 2017-03-01 - 2017-03-02 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Industry Support Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Speech, Engineering/Electro Acoustics, Signal Processing, and Related Topics |
| Paper Information | |
| Registration To | EA |
| Conference Code | 2017-03-SP-SIP-EA |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Indoor-environmental sound identification based on deep neural network with higher-dimensional features |
| Sub Title (in English) | |
| Keyword(1) | Environmental sound discrimination |
| Keyword(2) | Higher-dimentional feature |
| Keyword(3) | Deep neural network |
| Keyword(4) | Acoustic model |
| Keyword(5) | |
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| 1st Author's Name | Sakiko Mishima |
| 1st Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 2nd Author's Name | Yukoh Wakabayashi |
| 2nd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 3rd Author's Name | Takahiro Fukumori |
| 3rd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 4th Author's Name | Masato Nakayama |
| 4th Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 5th Author's Name | Takanobu Nishiura |
| 5th Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
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| Speaker | Author-1 |
| Date Time | 2017-03-01 12:40:00 |
| Presentation Time | 90 minutes |
| Registration for | EA |
| Paper # | EA2016-87, SIP2016-142, SP2016-82 |
| Volume (vol) | vol.116 |
| Number (no) | no.475(EA), no.476(SIP), no.477(SP) |
| Page | pp.31-36 |
| #Pages | 6 |
| Date of Issue | 2017-02-22 (EA, SIP, SP) |
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