Using Music and Emotion to Enable Effective Affective Computing

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 psychophysiological studies that permit the assembly of very large samples that cross numerous demographic boundaries, data collection in naturalistic environments, 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 characterizing 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 characteristics 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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2019

Author dc:creator

• Bortz, Brennon Christopher

Chair dc:contributor.committeechair

• Knapp, R. Benjamin

Committee members dc:contributor.committeemember

• Harrison, Steven R.
• Bradburn, Isabel S.
• Jaimovich, Javier
• McCrickard, D. Scott
• Lyon, Eric

• Affective Computing
• Music
• Psychophysiology
• Databases

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:21357

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/90888