Sound-based Respiratory Rhyme Estimation
碩士 === 國立中正大學 === 電機工程研究所 === 107 === This research proposes a sound-based detection to replace the method of traditional "Respiratory training". Compared with the traditional Triflow, this research intends to improve the inconvenience of carrying traditional Triflow and Hygiene issue that...
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ndltd-TW-107CCU004420852019-11-02T05:27:10Z http://ndltd.ncl.edu.tw/handle/w722am Sound-based Respiratory Rhyme Estimation 基於聲音之呼吸偵測 LIN, JIA-RONG 林家榮 碩士 國立中正大學 電機工程研究所 107 This research proposes a sound-based detection to replace the method of traditional "Respiratory training". Compared with the traditional Triflow, this research intends to improve the inconvenience of carrying traditional Triflow and Hygiene issue that the mouth often touches the suction tube by detecting respiratory sounds. Therefore, how to use the respiratory sound to reproduce the same effect as the Triflow is an important topic of this research. This research attempts to use MFCC and Perceptron to do machine learning to subdivide the respiratory sound into inspiratory, expiratory, and the respiratory interval. The experimental results show that after taking the respiratory signal characteristics by MFCC, the Perceptron model can be used to make Inference. In order to reproduce the realistic respiratory training effect, this paper proposes the calibration of the inspiratory capacity with sound energy further, and calibrates the difference of the inspiratory capacity corresponding to each person’s inspiratory sound to achieve the nearest to the effect of the Triflow. Finally, we can propose a respiratory training system that can replace the traditional Triflow. YEH, CHINGWEI LIN, TAY-JYI 葉經緯 林泰吉 2019 學位論文 ; thesis 43 zh-TW |
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碩士 === 國立中正大學 === 電機工程研究所 === 107 === This research proposes a sound-based detection to replace the method of traditional "Respiratory training". Compared with the traditional Triflow, this research intends to improve the inconvenience of carrying traditional Triflow and Hygiene issue that the mouth often touches the suction tube by detecting respiratory sounds. Therefore, how to use the respiratory sound to reproduce the same effect as the Triflow is an important topic of this research.
This research attempts to use MFCC and Perceptron to do machine learning to subdivide the respiratory sound into inspiratory, expiratory, and the respiratory interval. The experimental results show that after taking the respiratory signal characteristics by MFCC, the Perceptron model can be used to make Inference. In order to reproduce the realistic respiratory training effect, this paper proposes the calibration of the inspiratory capacity with sound energy further, and calibrates the difference of the inspiratory capacity corresponding to each person’s inspiratory sound to achieve the nearest to the effect of the Triflow. Finally, we can propose a respiratory training system that can replace the traditional Triflow.
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author2 |
YEH, CHINGWEI |
author_facet |
YEH, CHINGWEI LIN, JIA-RONG 林家榮 |
author |
LIN, JIA-RONG 林家榮 |
spellingShingle |
LIN, JIA-RONG 林家榮 Sound-based Respiratory Rhyme Estimation |
author_sort |
LIN, JIA-RONG |
title |
Sound-based Respiratory Rhyme Estimation |
title_short |
Sound-based Respiratory Rhyme Estimation |
title_full |
Sound-based Respiratory Rhyme Estimation |
title_fullStr |
Sound-based Respiratory Rhyme Estimation |
title_full_unstemmed |
Sound-based Respiratory Rhyme Estimation |
title_sort |
sound-based respiratory rhyme estimation |
publishDate |
2019 |
url |
http://ndltd.ncl.edu.tw/handle/w722am |
work_keys_str_mv |
AT linjiarong soundbasedrespiratoryrhymeestimation AT línjiāróng soundbasedrespiratoryrhymeestimation AT linjiarong jīyúshēngyīnzhīhūxīzhēncè AT línjiāróng jīyúshēngyīnzhīhūxīzhēncè |
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1719285369769295872 |