Bardo: Emotion-Based Music Recommendation for Tabletop Role-Playing Games

Authors

  • Rafael Padovani Universidade Federal de Viçosa
  • Lucas Ferreira University of California, Santa Cruz
  • Levi Lelis Universidade Federal de Viçosa

DOI:

https://doi.org/10.1609/aiide.v13i1.12958

Keywords:

emotion model, natural language processing, music selection, supervised learning, tabletop games

Abstract

In this paper we introduce Bardo, a real-time intelligent system to automatically select the background music for tabletop role-playing games. Bardo uses an off-the-shelf speech recognition system to transform into text what the players say during a game session, and a supervised learning algorithm to classify the text into an emotion. Bardo then selects and plays as background music a song representing the classified emotion. We evaluate Bardo with a Dungeons and Dragons (D&D) campaign available on YouTube. Accuracy experiments show that a simple Naive Bayes classifier is able to obtain good prediction accuracy in our classification task. A user study in which people evaluated edited versions of the D&D videos suggests that Bardo's selections can be better than those used in the original videos of the campaign.

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Published

2021-06-25

How to Cite

Padovani, R., Ferreira, L., & Lelis, L. (2021). Bardo: Emotion-Based Music Recommendation for Tabletop Role-Playing Games. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 13(1), 214-220. https://doi.org/10.1609/aiide.v13i1.12958