Photo Sequences of Varying Emotion: Optimization with a Valence-Arousal Annotated Dataset

2021 ◽  
Vol 11 (2) ◽  
pp. 1-19
Author(s):  
Christos Mousas ◽  
Claudia Krogmeier ◽  
Zhiquan Wang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
User Study ◽  
Optimization Techniques ◽  
Design Parameters ◽  
Monte Carlo Optimization ◽  
Total Cost ◽  
Emotional Model ◽  
Valence And Arousal ◽  
Total Cost Function

Synthesizing photo products such as photo strips and slideshows using a database of images is a time-consuming and tedious process that requires significant manual work. To overcome this limitation, we developed a method that automatically synthesizes photo sequences based on several design parameters. Our method considers the valence and arousal ratings of images in conjunction with parameters related to both the visual consistency of the synthesized photo sequence and the progression of valence and arousal throughout the photo sequence. Our method encodes valence, arousal, and visual consistency parameters as cost terms into a total cost function while applying a Markov chain Monte Carlo optimization techniques called simulated annealing to synthesize the photo sequence based on user-defined target objectives in a few seconds. As our method was developed for the synthesis of photo sequences using the valence-arousal emotional model, a user study was conducted to evaluate the efficacy of the synthesized photo sequences in triggering valence-arousal ratings as expected. Our results indicate that the proposed method synthesizes photo sequences in which valence and arousal dimensions are perceived as expected by participants; however, valence may be more appropriately perceived than arousal.

Bayesian Applications in Auditory Research

2019 ◽  
Vol 62 (3) ◽  
pp. 577-586 ◽  
Author(s):  
Garnett P. McMillan ◽  
John B. Cannon
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Bayesian Methods ◽  
Prior Distribution ◽  
Interim Analysis ◽  
Iterative Process ◽  
Bayes Theorem ◽  
Sound Therapy ◽  
The Many ◽  
Auditory Research

Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592

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