Markov Chain Based Procedural Music Generator with User Chosen Mood Compatibility

Abstract

Music is a phenomenon common in most human cultures. In a lot of cases, music is played as an accompaniment to other forms of art and activities, such as movies, video games, theatre, or as simple as background music for restaurants and museums. The music in these cases serve to set the mood the artist intends to make the consumers feel. According to previous studies, there is indeed a link between human emotions and music. One of the case that makes people feel different emotions is through the composition of the music itself. Procedural content generation is a field in computer science which creates a random content or art algorithmically within a set constraint. The goal of this study is to create a system that could randomly generate music that fits the mood from a manual user input. Markov chain is a stochastic model used in modeling the components of music composition. For the procedurally generated to fulfill the mood set by the user, different parameter values for each composition component is allotted for each mood. These components include tempo, pitch range, note values, chord type dominance, and melody notes. The implementation of the procedural music generation system is then evaluated by survey and experiments. The evaluation yielded results which assures the capability of the music generation system to fit the mood input.