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

Padovani, Rafael R. (Universidade Federal de Viçosa) | Ferreira, Lucas N. (University of California, Santa Cruz) | Lelis, Levi H. S. (Universidade Federal de Viçosa)

AAAI Conferences 

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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