[FARM24] A Progressive-Adaptive Music Generator (PAMG): An Approach to Interactive Procedural(…) скачать в хорошем качестве

[FARM24] A Progressive-Adaptive Music Generator (PAMG): An Approach to Interactive Procedural(…)

A Progressive-Adaptive Music Generator (PAMG): An Approach to Interactive Procedural Music for Videogames (Video, FARM 2024) Alvaro Eduardo Lopez Duarte (University of California at Riverside, USA) Abstract: Procedural music generation in video game is rarely used in development despite its potential support for interactive narratives, and falls behind broadly-used procedural methods like graphic management. Currently, developers rely on preproduced audio sequences, focusing on sound quality while balancing storage and variety. Techniques like layering and re-sequencing are standard through sound design platforms like FMOD and Wwise, allowing adaptation and interactivity in gameplay. However, this can lead to repetitive audio, which becomes particularly noticeable in extended gaming sessions, diminishing the impact of musical storytelling. This study introduces the ProgressiveAdaptive Music Generator (PAMG), an algorithmic system that generates continuous music streams based on gameplay variables, seamlessly transitioning between moods, styles, and tension levels. It includes motivation, theoretical framework in the field of generative music, overview of the system structure, and a preliminary implementation test involving a Trial Game that compares conventional implementation with PAMG. In the discussion section, it addresses test results and issues, and future work. The study reveals a preference for PAMG’s music over traditional methods in certain aspects, although results are not conclusive. Article: https://doi.org/10.1145/3677996.3678291 ORCID: https://orcid.org/0000-0003-0820-8458 Video Tags: AI Music, Game Music, Procedural Music, icfpws24farmmain-p53-p, doi:10.1145/3677996.3678291, orcid:0000-0003-0820-8458 Presentation at the FARM 2024 conference, September 2, 2024, https://icfp24.sigplan.org/home/farm-... Sponsored by ACM SIGPLAN, https://www.sigplan.org/