Abstract
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.
Highlights
Empathy, a phenomenon of characterizing our understanding and sharing of others’ feelings, is vital to our everyday communication and survival in a social environment [1]
The assumption we have made is that the extracted audio feature can distinguish between empathetic and non-empathetic videos
The mel-frequency cepstral coefficients (MFCCs) of a signal are a small set of features that concisely describe the overall shape of a spectral envelope
Summary
A phenomenon of characterizing our understanding and sharing of others’ feelings, is vital to our everyday communication and survival in a social environment [1]. Empathy evaluation has many methods but can be mainly classified into three categories: subjective size evaluation, image processing, and bio-signals [2]. A four-camera vision system was established to sample a target from different perspectives. In this system, a global calibration technique was deployed to correlate each individual system. The subjective evaluation method usually uses a questionnaire but has limitations due to subjective ambiguity and individual differences. In order to compensate for its limitations, researchers use objective methods to evaluate empathy
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