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Paper Abstract and Keywords
Presentation 2018-02-15 15:45
A Note on Estimation of Users' Emotion Evoked During Listening to Music -- Performance Improvement Based on Deep Learning Method --
Hakusyou Dan, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents a method that estimates users’ emotion evoked during listening to music. In our method, we use audio features from the music data and Heart Rate Variability (HRV) features from the listening users and then calculate their canonical features. Next, we use the canonical features to build a classifier for the emotion estimation. Note that the audio features are extracted from the middle layer of the convolutional neural networks. Furthermore, the HRV features are extracted from the middle layer of the recurrent neural networks. We expect that by using the canonical features, more successful classification become realistic compared to the conventional methods. In the experiments, we compare the results of using the canonical features and those of several methods. In addition, the validity of the proposed method is confirmed.
Keyword (in Japanese) (See Japanese page) 
(in English) music / emotion estimation / audio features / HRV features / deep learning / / /  
Reference Info. ITE Tech. Rep., vol. 42, no. 4, ME2018-21, pp. 201-206, Feb. 2018.
Paper # ME2018-21 
Date of Issue 2018-02-08 (MMS, HI, ME, AIT) 
ISSN Print edition: ISSN 1342-6893  Online edition: ISSN 2424-1970
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Conference Information
Conference Date 2018-02-15 - 2018-02-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To ME 
Conference Code 2018-02-ITS-IE-MMS-HI-ME-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Note on Estimation of Users' Emotion Evoked During Listening to Music 
Sub Title (in English) Performance Improvement Based on Deep Learning Method 
Keyword(1) music  
Keyword(2) emotion estimation  
Keyword(3) audio features  
Keyword(4) HRV features  
Keyword(5) deep learning  
1st Author's Name Hakusyou Dan  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Takahiro Ogawa  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Miki Haseyama  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Date Time 2018-02-15 15:45:00 
Presentation Time 15 
Registration for ME 
Paper # ITE-MMS2018-21,ITE-HI2018-21,ITE-ME2018-21,ITE-AIT2018-21 
Volume (vol) ITE-42 
Number (no) no.4 
Page pp.201-206 
#Pages ITE-6 
Date of Issue ITE-MMS-2018-02-08,ITE-HI-2018-02-08,ITE-ME-2018-02-08,ITE-AIT-2018-02-08 

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