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Using Music and Emotion to Enable Effective Affective Computing Brennon Christopher Bortz

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Using Music and Emotion to Enable Effective Affective Computing Brennon Christopher Bortz USINGMUSICANDEMOTIONTOENABLEEFFECTIVEAFFECTIVE COMPUTING brennon christopher bortz Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science and Applications R. Benjamin Knapp, Chair D. Scott McCrickard Steve R. Harrison Eric D. Lyon Isabel S. Bradburn Javier E. Jaimovich 15 May 2019 Keywords: Affective Computing, Music, Psychophysiology, Databases ABSTRACT—ACADEMIC Using Music and Emotion to Enable Effective Affective Computing Brennon Christopher Bortz The computing devices with which we interact daily continue to become ever smaller, intelligent, and pervasive. Not only are they becoming more intelligent, but some are developing awareness of a user’s affective state. Affective computing—computing that in some way senses, expresses, or modifies affect—is still a field very much in its youth. While progress has been made, the field is still limited by the need for larger sets of diverse, naturalistic, and multimodal data. This work first considers effective strategies for designing psychophysi- ological studies that permit the assembly of very large samples that cross numerous demographic boundaries, data collection in naturalistic envi- ronments, distributed study locations, rapid iterations on study designs, and the simultaneous investigation of multiple research questions. It then explores how commodity hardware and general-purpose software tools can be used to record, represent, store, and disseminate such data. As a realization of these strategies, this work presents a new database from the Emotion in Motion (EiM) study of human psychophysiological response to musical affective stimuli comprising over 23,000 participants and nearly 67,000 psychophysiological responses. Because music presents an excellent tool for the investigation of human response to affective stimuli, this work uses this wealth of data to explore how to design more effective affective computing systems by character- izing the strongest responses to musical stimuli used in EiM. This work identifies and characterizes the strongest of these responses, with a focus on modeling the characteristics of listeners that make them more or less prone to demonstrating strong physiological responses to music stimuli. This dissertation contributes the findings from a number of explorations of the relationships between strong reactions to music and the characteris- tics and self-reported affect of listeners. It demonstrates not only that such relationships do exist, but takes steps toward automatically predicting whether or not a listener will exhibit such exceptional responses. Second, this work contributes a flexible strategy and functional system for both successfully executing large-scale, distributed studies of psychophysiology and affect; and for synthesizing, managing, and disseminating the data collected through such efforts. Finally, and most importantly, this work presents the EiM database itself. ABSTRACT—GENERALAUDIENCE Using Music and Emotion to Enable Effective Affective Computing Brennon Christopher Bortz The computing devices with which we interact daily continue to become ever smaller, intelligent, and pervasive. Not only are they becoming more intelligent, but some are developing awareness of a user’s affective state. Affective computing—computing that in some way senses, expresses, or modifies affect—is still a field very much in its youth. While progress has been made, the field is still limited by the need for larger sets of diverse, naturalistic, and multimodal data. This dissertation contributes the findings from a number of explorations of the relationships between strong reactions to music and the characteris- tics and self-reported affect of listeners. It demonstrates not only that such relationships do exist, but takes steps toward automatically predicting whether or not a listener will exhibit such exceptional responses. Second, this work contributes a flexible strategy and functional system for both successfully executing large-scale, distributed studies of psychophysiology and affect; and for synthesizing, managing, and disseminating the data collected through such efforts. Finally, and most importantly, this work presents the Emotion in Motion (EiM) (a study of human affective/psy- chophysiological response to musical stimuli) database comprising over 23,000 participants and nearly 67,000 psychophysiological responses. For Whit, Bug, Nugget, and whoever is next... v I think music is the greatest art form that exists, and I think people listen to music for different reasons, and it serves different purposes. Some of it is background music, and some of it is things that might affect a person’s day, if not their life, or change an attitude. The best songs are the ones that make you feel something. — Eddie Vedder ACKNOWLEDGMENTS First, I’m indebted to my advisor, Ben Knapp, with whom I’ve had the pleasure of working for more than a decade now. Ben, for your continual patience with me and encouragement throughout this work you have my sincere gratitude. There have been times, certainly, when I’ve been nothing short of exasperating. Thank you for spurring me on toward the goal. Your insight, knowledge, and creativity have been indispensable during this work. These more than ten years working with Ben started on an entirely different continent. We began working together at the Sonic Arts Research Centre at Queen’s University Belfast—a place that will always remain very special to me. There, I was fortunate to work with and alongside a number of exceptional people, many of whom remain friends to this day. In particular, my coworkers in the Music, Sensors, and Emotion research group made my time in Northern Ireland a wonderful one. Miguel Ortiz Pérez, Niall Coghlan, Cavan Fyans, Adnan Marquez-Borbon, Michael Gurevich, Paul Stapleton, and Nick Gillian, you all are tremendous people. Michael Alcorn, I’m thankful for meeting you many years ago, which kicked off this wild adventure. In particular, one of the finest people that I have been privileged to have the opportunity to know from my time there is Javier Jaimovich. Our work that began there has continued to today as a fruitful collaboration. Javier, your friendship, diligence, and creativity have been an inspiration for me. I’m very thankful to call you a friend and to have worked with you, and I look forward to continuing to do so. This dissertation reaches for inspiration across a number of disciplinary boundaries. I’m grateful to my committee members who come from equally diverse disciplines: Ben Knapp, Javier Jaimovich, Isabel Brad- burn, Steve Harrison, Scott McCrickard, and Eric Lyon. I appreciate the insights and wisdom that you have brought to this effort. What began in Belfast, Northern Ireland ends here, in Blacksburg, Vir- ginia, where I came to work as the “inaugural” doctoral student in the Institute for Creativity, Arts, and Technology at Virginia Tech. Resetting vi progress on my Ph.D. after already having worked on it for several years made for some unnerving times, but the faculty, staff, and my fellow stu- dents at ICAT helped prove it was the right decision, in the end. Melissa, Holly, Deba, Tanner, Zach, Liz, and so many others, you are all fantastic. To my mother, father, and brother, I know that many of my choices in life have given you reason for pause over the years. Thank you for your continual encouragement, and for the example of what an incredible family is. Ryan, your perseverance, especially, is a constant motivation for me. My deepest gratitude is owed to my two girls, Aisling and Kella, and my wife, Whitney. I’ve missed too many important moments in your childhoods, girls. Don’t worry, Daddy’s “stupid dissertation” is done. Whitney, you’ve been a motivation and inspiration to me since all the way back at the swing in front of Darnell Hall. Thank you for your tireless encouragement, for being an incredible mother to our children, for your long-suffering patience, and in general, for putting up with my nonsense. I love you. vii CONTENTS 1 introduction1 1.1 The Power of Music 1 1.2 The Potential of Affective Computing 2 1.3 The Relationship Between Music and Affect 3 1.4 Psychophysiology 4 1.5 Research Aims and Questions 6 1.5.1 Gaps in Knowledge 7 1.5.2 Research Question 1 (RQ1) 8 1.5.3 Research Question 2 (RQ2) 9 1.5.4 Research Question 3 (RQ3) 10 1.6 Summary 11 2 related work 13 2.1 Affect 13 2.1.1 What is an Emotion? 13 2.1.2 Theories of Emotion 14 2.2 Music and Affect 23 2.2.1 Reliable Perception of Expressed Musical Emotion 23 2.2.2 Perceived versus Felt Emotion 25 2.2.3 Relationships Between Musical Features and Af- fect 28 2.3 Summary 32 3 emotion in motion 33 3.1 Study Overview 33 3.2 Study Goals 34 3.3 Iterative Design 35 3.4 Apparatus 35 3.5 Methods 37 3.5.1 Data Collected 37 3.5.2 Musical Stimuli 44 3.5.3 Stimuli Selection 46 3.6 Summary 48 4 a flexible system for large-scale, distributed stud- ies of affect 49 4.1 Similar Systems 50 4.1.1 Research Resource for Complex Physiologic Sig- nals 50 4.1.2 MAHNOB-HCI Database 51 4.1.3 MMI Facial Expression Database 52 4.1.4 DEAP Database 53 viii contents ix 4.1.5 Summary of Similar Systems 54 4.2 Issues with the Original EiM System 54 4.2.1 Issues with the Study Apparatus 55 4.2.2 Issues with Data Synthesis, Management, and Dis- semination 57 4.2.3 Specific Issues with and Changes to the EiM Study Design 58 4.3 Requirements for a Flexible System 60 4.3.1 Requirements for the Data
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