An Empathy Evaluation System Using Spectrogram Image Features of Audio

Watching videos online has become part of a relaxed lifestyle. The music in videos has a sensitive influence on human emotions, perception, and imaginations, which can make people feel relaxed or sad, and so on. Therefore, it is particularly important for people who make advertising videos to unders...

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Autores principales:	Jing Zhang, Xingyu Wen, Ayoung Cho, Mincheol Whang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	empathy evaluation audio processing MFCC machine learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/fa39bda1ee17427dbfc19654d63e688c
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id	oai:doaj.org-article:fa39bda1ee17427dbfc19654d63e688c
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spelling	oai:doaj.org-article:fa39bda1ee17427dbfc19654d63e688c2021-11-11T19:07:14ZAn Empathy Evaluation System Using Spectrogram Image Features of Audio10.3390/s212171111424-8220https://doaj.org/article/fa39bda1ee17427dbfc19654d63e688c2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7111https://doaj.org/toc/1424-8220Watching videos online has become part of a relaxed lifestyle. The music in videos has a sensitive influence on human emotions, perception, and imaginations, which can make people feel relaxed or sad, and so on. Therefore, it is particularly important for people who make advertising videos to understand the relationship between the physical elements of music and empathy characteristics. The purpose of this paper is to analyze the music features in an advertising video and extract the music features that make people empathize. This paper combines both methods of the power spectrum of MFCC and image RGB analysis to find the audio feature vector. In spectral analysis, the eigenvectors obtained in the analysis process range from blue (low range) to green (medium range) to red (high range). The machine learning random forest classifier is used to classify the data obtained by machine learning, and the trained model is used to monitor the development of an advertisement empathy system in real time. The result is that the optimal model is obtained with the training accuracy result of 99.173% and a test accuracy of 86.171%, which can be deemed as correct by comparing the three models of audio feature value analysis. The contribution of this study can be summarized as follows: (1) the low-frequency and high-amplitude audio in the video is more likely to resonate than the high-frequency and high-amplitude audio; (2) it is found that frequency and audio amplitude are important attributes for describing waveforms by observing the characteristics of the machine learning classifier; (3) a new audio extraction method is proposed to induce human empathy. That is, the feature value extracted by the method of spectrogram image features of audio has the most ability to arouse human empathy.Jing ZhangXingyu WenAyoung ChoMincheol WhangMDPI AGarticleempathy evaluationaudio processingMFCCmachine learningChemical technologyTP1-1185ENSensors, Vol 21, Iss 7111, p 7111 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	empathy evaluation audio processing MFCC machine learning Chemical technology TP1-1185
spellingShingle	empathy evaluation audio processing MFCC machine learning Chemical technology TP1-1185 Jing Zhang Xingyu Wen Ayoung Cho Mincheol Whang An Empathy Evaluation System Using Spectrogram Image Features of Audio
description	Watching videos online has become part of a relaxed lifestyle. The music in videos has a sensitive influence on human emotions, perception, and imaginations, which can make people feel relaxed or sad, and so on. Therefore, it is particularly important for people who make advertising videos to understand the relationship between the physical elements of music and empathy characteristics. The purpose of this paper is to analyze the music features in an advertising video and extract the music features that make people empathize. This paper combines both methods of the power spectrum of MFCC and image RGB analysis to find the audio feature vector. In spectral analysis, the eigenvectors obtained in the analysis process range from blue (low range) to green (medium range) to red (high range). The machine learning random forest classifier is used to classify the data obtained by machine learning, and the trained model is used to monitor the development of an advertisement empathy system in real time. The result is that the optimal model is obtained with the training accuracy result of 99.173% and a test accuracy of 86.171%, which can be deemed as correct by comparing the three models of audio feature value analysis. The contribution of this study can be summarized as follows: (1) the low-frequency and high-amplitude audio in the video is more likely to resonate than the high-frequency and high-amplitude audio; (2) it is found that frequency and audio amplitude are important attributes for describing waveforms by observing the characteristics of the machine learning classifier; (3) a new audio extraction method is proposed to induce human empathy. That is, the feature value extracted by the method of spectrogram image features of audio has the most ability to arouse human empathy.
format	article
author	Jing Zhang Xingyu Wen Ayoung Cho Mincheol Whang
author_facet	Jing Zhang Xingyu Wen Ayoung Cho Mincheol Whang
author_sort	Jing Zhang
title	An Empathy Evaluation System Using Spectrogram Image Features of Audio
title_short	An Empathy Evaluation System Using Spectrogram Image Features of Audio
title_full	An Empathy Evaluation System Using Spectrogram Image Features of Audio
title_fullStr	An Empathy Evaluation System Using Spectrogram Image Features of Audio
title_full_unstemmed	An Empathy Evaluation System Using Spectrogram Image Features of Audio
title_sort	empathy evaluation system using spectrogram image features of audio
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fa39bda1ee17427dbfc19654d63e688c
work_keys_str_mv	AT jingzhang anempathyevaluationsystemusingspectrogramimagefeaturesofaudio AT xingyuwen anempathyevaluationsystemusingspectrogramimagefeaturesofaudio AT ayoungcho anempathyevaluationsystemusingspectrogramimagefeaturesofaudio AT mincheolwhang anempathyevaluationsystemusingspectrogramimagefeaturesofaudio AT jingzhang empathyevaluationsystemusingspectrogramimagefeaturesofaudio AT xingyuwen empathyevaluationsystemusingspectrogramimagefeaturesofaudio AT ayoungcho empathyevaluationsystemusingspectrogramimagefeaturesofaudio AT mincheolwhang empathyevaluationsystemusingspectrogramimagefeaturesofaudio
_version_	1718431622032785408

An Empathy Evaluation System Using Spectrogram Image Features of Audio

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