AUTOMATIC MANIPULATION OF MUSIC TO EXPRESS DESIRED EMOTIONS

We are developing a computational system that produces music expressing desired emotions. This paper is focused on the automatic transformation of 2 emotional dimensions of music (valence and arousal) by changing musical features: tempo, pitch register, musical scales, instruments and articulation. Transformation is supported by 2 regression models, each with weighted mappings between an emotional dimension and music features. We also present 2 algorithms used to sequence segments. We made an experiment with 37 listeners who were asked to label online 2 emotional dimensions of 132 musical segments. Data coming from this experiment was used to test the effectiveness of the transformation algorithms and to update the weights of features of the regression models. Tempo and pitch register proved to be relevant on both valence and arousal. Musical scales and instruments were also relevant for both emotional dimensions but with a lower impact. Staccato articulation influenced only valence. 1