Using deep learning algorithm to predict the time series data with EEG and music data

碩士 === 輔仁大學 === 資訊工程學系碩士班 === 105 === This paper studies the prediction of continuous music emotion values based on physiological signals and music data. We use a two-dimensional emotion model. Valence and Arousal are predicted respectively. We use the general recurrent neural network and the more c...

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Bibliographic Details
Main Authors:	YAN, GIA-CUN, 顏嘉村
Other Authors:	Hsu, Jia-Lien
Format:	Others
Language:	zh-TW
Published:	2017
Online Access:	http://ndltd.ncl.edu.tw/handle/9syhsv

id	ndltd-TW-105FJU00396040
record_format	oai_dc
spelling	ndltd-TW-105FJU003960402019-05-15T23:32:16Z http://ndltd.ncl.edu.tw/handle/9syhsv Using deep learning algorithm to predict the time series data with EEG and music data 利用深度學習演算法預測時間序列資料以腦波與音樂資料為例 YAN, GIA-CUN 顏嘉村碩士輔仁大學資訊工程學系碩士班 105 This paper studies the prediction of continuous music emotion values based on physiological signals and music data. We use a two-dimensional emotion model. Valence and Arousal are predicted respectively. We use the general recurrent neural network and the more complex sequence to sequence model to do numerical prediction. Based on the EEG data, the lowest MSE of Valence is 0.0047 and the lowest MSE of Arousal is 0.0093. Based on the music data, the lowest MSE of Valence is 0.0028 and the lowest MSE of Arousal is 0.0073. Hsu, Jia-Lien 徐嘉連 2017 學位論文 ; thesis 36 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 輔仁大學 === 資訊工程學系碩士班 === 105 === This paper studies the prediction of continuous music emotion values based on physiological signals and music data. We use a two-dimensional emotion model. Valence and Arousal are predicted respectively. We use the general recurrent neural network and the more complex sequence to sequence model to do numerical prediction. Based on the EEG data, the lowest MSE of Valence is 0.0047 and the lowest MSE of Arousal is 0.0093. Based on the music data, the lowest MSE of Valence is 0.0028 and the lowest MSE of Arousal is 0.0073.
author2	Hsu, Jia-Lien
author_facet	Hsu, Jia-Lien YAN, GIA-CUN 顏嘉村
author	YAN, GIA-CUN 顏嘉村
spellingShingle	YAN, GIA-CUN 顏嘉村 Using deep learning algorithm to predict the time series data with EEG and music data
author_sort	YAN, GIA-CUN
title	Using deep learning algorithm to predict the time series data with EEG and music data
title_short	Using deep learning algorithm to predict the time series data with EEG and music data
title_full	Using deep learning algorithm to predict the time series data with EEG and music data
title_fullStr	Using deep learning algorithm to predict the time series data with EEG and music data
title_full_unstemmed	Using deep learning algorithm to predict the time series data with EEG and music data
title_sort	using deep learning algorithm to predict the time series data with eeg and music data
publishDate	2017
url	http://ndltd.ncl.edu.tw/handle/9syhsv
work_keys_str_mv	AT yangiacun usingdeeplearningalgorithmtopredictthetimeseriesdatawitheegandmusicdata AT yánjiācūn usingdeeplearningalgorithmtopredictthetimeseriesdatawitheegandmusicdata AT yangiacun lìyòngshēndùxuéxíyǎnsuànfǎyùcèshíjiānxùlièzīliàoyǐnǎobōyǔyīnlèzīliàowèilì AT yánjiācūn lìyòngshēndùxuéxíyǎnsuànfǎyùcèshíjiānxùlièzīliàoyǐnǎobōyǔyīnlèzīliàowèilì
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Using deep learning algorithm to predict the time series data with EEG and music data

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