Statistical Vector Model for the Audience Emotional Response Examination with Affective Computing Based on Conductor’s Expressive
Keywords:
Conductors Expressive, Audience, Emotional Response, Affective Computing, Deep Learning, Feature ExtractionAbstract
Audience Emotional Response refers to the range of emotional reactions, feelings, and sentiments that individuals in an audience experience while engaging with a particular piece of content, such as movies, music, art, advertisements, or any form of media or communication. Understanding and analyzing audience emotional responses are essential for various fields, including psychology, marketing, entertainment, and media research. Hence, this paper explores the fusion of youaffective computing, conductor's expressive performances, and audience emotional responses, introducing the innovative Statistical Feature Extraction Vector Deep Learning (SFEVDL) approach. The research capitalizes on the potential of SFEVDL to unravel the intricate emotional intricacies inherent in facial expressions, thereby shedding light on the multifaceted connection between conductor gestures and audience emotions. The study encompasses a comprehensive methodology involving data preprocessing, feature extraction, and deep learning techniques. By harnessing a diverse array of datasets, including EmoReact Dataset, Conductor Archives, Public Performances, Collaborative Settings, Collection, and Mixed Modal contexts, the research illustrates the robustness and adaptability of the SFEVDL approach. The empirical results reveal accurate emotion prediction, aligned emotional responses, and the inherent complexities of interpreting emotions in artistic scenarios. This research serves as a stepping stone in the realm of affective computing, illuminating the pathways for future advancements in the understanding of human emotions, artistic expression, and their intersection with technological innovation.
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Yang, S., Reed, C. N., Chew, E., & Barthet, M. (2021). Examining emotion perception agreement in live music performance. IEEE transactions on affective computing.
Zhang, Y., Zhao, G., Shu, Y., Ge, Y., Zhang, D., Liu, Y. J., & Sun, X. (2021). Cped: A chinese positive emotion database for emotion elicitation and analysis. IEEE Transactions on Affective Computing.
Tian, L., Oviatt, S., Muszynski, M., Chamberlain, B., Healey, J., & Sano, A. (2022). Applied Affective Computing.
Hasnul, M. A., Aziz, N. A. A., Alelyani, S., Mohana, M., & Aziz, A. A. (2021). Electrocardiogram-based emotion recognition systems and their applications in healthcare—A review. Sensors, 21(15), 5015.
Zhang, S., Chen, N., & Hsu, C. H. (2021). Facial expressions versus words: Unlocking complex emotional responses of residents toward tourists. Tourism Management, 83, 104226.
Bontempi, P., Canazza, S., Carnovalini, F., & Rodà, A. (2023). Research in Computational Expressive Music Performance and Popular Music Production: A Potential Field of Application?. Multimodal Technologies and Interaction, 7(2), 15.
Bontempi, P., Canazza, S., Carnovalini, F., & Rodà, A. (2023). Research in Computational Expressive Music Performance and Popular Music Production: A Potential Field of Application?. Multimodal Technologies and Interaction, 7(2), 15.
Yang, S., Reed, C. N., Chew, E., & Barthet, M. (2021). Examining emotion perception agreement in live music performance. IEEE transactions on affective computing.
Tian, L., Oviatt, S., Muszynski, M., Chamberlain, B., Healey, J., & Sano, A. (2022). Applied Affective Computing.
Hasnul, M. A., Aziz, N. A. A., Alelyani, S., Mohana, M., & Aziz, A. A. (2021). Electrocardiogram-based emotion recognition systems and their applications in healthcare—A review. Sensors, 21(15), 5015.
Bontempi, P., Canazza, S., Carnovalini, F., & Rodà, A. (2023). Research in Computational Expressive Music Performance and Popular Music Production: A Potential Field of Application?. Multimodal Technologies and Interaction, 7(2), 15.
Zhang, Y., Zhao, G., Shu, Y., Ge, Y., Zhang, D., Liu, Y. J., & Sun, X. (2021). Cped: A chinese positive emotion database for emotion elicitation and analysis. IEEE Transactions on Affective Computing.
Nápoles, J., Silvey, B. A., & Montemayor, M. (2021). The influences of facial expression and conducting gesture on college musicians' perceptions of choral conductor and ensemble expressivity. International Journal of Music Education, 39(2), 260-271.
Springer, D. G., Silvey, B. A., Doshier, N., & Hall, F. (2023). Effects of Conducting With or Without a Musical Score on Observers’ Perceptions of Conductors. Journal of Research in Music Education, 00224294231173318.
Jansson, D., Haugland Balsnes, A., & Durrant, C. (2022). The gesture enigma: Reconciling the prominence and insignificance of choral conductor gestures. Research Studies in Music Education, 44(3), 509-526.
Shuford, B. (2022). A Survey of African American Female Choral Conductors on Spirituality and" It Factor" Choral Performances (Doctoral dissertation, Auburn University).
Silvey, B. A., Montemayor, M., & Davis, A. (2022). An examination of collegiate musicians’ ability to discern conductor intent. Psychology of Music, 03057356221141734.
García‐Fernández, L., Romero‐Ferreiro, V., Padilla, S., David López‐Roldán, P., Monzó‐García, M., & Rodriguez‐Jimenez, R. (2021). Gender differences in emotional response to the COVID‐19 outbreak in Spain. Brain and behavior, 11(1), e01934.
Serpico, M., Rovai, D., Wilke, K., Lesniauskas, R., Garza, J., & Lammert, A. (2021). Studying the emotional response to insects food products. Foods, 10(10), 2404.
Serpico, M., Rovai, D., Wilke, K., Lesniauskas, R., Garza, J., & Lammert, A. (2021). Studying the emotional response to insects food products. Foods, 10(10), 2404.
Gall, D., Roth, D., Stauffert, J. P., Zarges, J., & Latoschik, M. E. (2021). Embodiment in virtual reality intensifies emotional responses to virtual stimuli. Frontiers in Psychology, 12, 674179.
Heffner, J., Vives, M. L., & FeldmanHall, O. (2021). Emotional responses to prosocial messages increase willingness to self-isolate during the COVID-19 pandemic. Personality and Individual Differences, 170, 110420.
Shetty, Y., Mehta, S., Tran, D., Soni, B., & McDaniel, T. (2021). Emotional Response to Vibrothermal Stimuli. Applied Sciences, 11(19), 8905.
Bischetti, L., Canal, P., & Bambini, V. (2021). Funny but aversive: A large-scale survey of the emotional response to Covid-19 humor in the Italian population during the lockdown. Lingua, 249, 102963.
Roca, J., Canet‐Vélez, O., Cemeli, T., Lavedán, A., Masot, O., & Botigué, T. (2021). Experiences, emotional responses, and coping skills of nursing students as auxiliary health workers during the peak COVID‐19 pandemic: A qualitative study. International journal of mental health nursing, 30(5), 1080-1092.
Lim, L. A., Dawson, S., Gašević, D., Joksimović, S., Pardo, A., Fudge, A., & Gentili, S. (2021). Students’ perceptions of, and emotional responses to, personalised learning analytics-based feedback: an exploratory study of four courses. Assessment & Evaluation in Higher Education, 46(3), 339-359.
Papautsky, E. L., & Hamlish, T. (2021). Emotional response of US breast cancer survivors during the COVID-19 pandemic. Cancer Investigation, 39(1), 3-8.
Hameleers, M. (2021). Prospect theory in times of a pandemic: The effects of gain versus loss framing on risky choices and emotional responses during the 2020 coronavirus outbreak–Evidence from the US and the Netherlands. Mass Communication and Society, 24(4), 479-499.
Frandsen, S., & Morsing, M. (2022). Behind the stigma shield: frontline employees’ emotional response to organizational event stigma at work and at home. Journal of Management Studies, 59(8), 1987-2023.
Hamilton, O. S., Cadar, D., & Steptoe, A. (2021). Systemic inflammation and emotional responses during the COVID-19 pandemic. Translational Psychiatry, 11(1), 626.
Song, H., Kim, M., & Choe, Y. (2021). Structural relationships among mega-event experiences, emotional responses, and satisfaction: Focused on the 2014 Incheon Asian Games. In Current Issues in Asian Tourism: Volume II (pp. 139-145). Routledge.
Patnaude, L., Lomakina, C. V., Patel, A., & Bizel, G. (2021). Public emotional response on the black lives matter movement in the summer of 2020 as analyzed through twitter. International Journal of Marketing Studies, 13(1), 1-69.
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