Video game developers attempt to convey moods to emphasize their game's narrative. Events that occur within the game usually convey success or failure in some way meaningful to the story's progress. Ideally, when these events occur, the intended change in mood should be perceivable to the player. One way of doing so is to change the music. This requires musical tracks to represent many possible moods, states and game events. This can be very taxing on composers, and encoding the control flow (when to transition) of the tracks can prove to be tricky as well. This thesis presents AUD.js, a system developed for procedural music generation for JavaScript-based web games. By taking input from game events, the system can create music corresponding to various Western perceptions of music mood. The system was trained with classic video game music. Game development students rated the mood of 80 pieces, after which statistical representations of those pieces were extracted and added into AUD.js. AUD.js can adapt its generated music to new sets of input parameters, thereby updating the perceived mood of the generated music at runtime. We conducted A/B tests comparing static music, both composed and computer-generated, to dynamically adapting music. We find that AUD.js provides reasonably effective music for games, but that adaptiveness of the music does not necessarily improve player experience over composed music. By conducting a user study during Global Game Jam 2014, we also find that since AUD.js provides a software solution to music composition, it can be a useful tool for game music integration under time pressure.

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Music listening is considered one of the most ubiquitous activities in everyday life, and one of the main reasons why people listen is to affect and regulate their mood. The vast availability and unlimited access of music has made it difficult to find relevant music that fits both the context and the preferences of the music listener. The aim of this project was to investigate the personalized relationship between music and mood using everyday technologies, focusing on how a listening experience could be adapted to the desired affect of a music listener while also taking the user’s individual listening history into account. In large, the project concentrated on the possibility of using context-aware music recommendation to generate personalized playlists by focusing on the audio features and corresponding mood of the music. A web-based application was developed to act as a prototype for the study, where the application allowed users to connect to Spotify, pick a desired mood and generate a playlist. By allowing people to access music in this personalized way, a user study could be conducted in order to investigate their music listening while incorporating this recommendation tool. The findings showed that the users’ found the experience to be engaging in that they could use the application as a companion to everyday tasks in addition to it being a tool for getting new, personalized music recommendations. Overall, the participants also found the generated playlists to be accurate to their music preferences and desired affective state.