Abstract
We propose to significantly extend our work in EEG-based emotion detection for automated expressive performances of algorithmically composed music for affective communication and induction. This new system involves music composed and expressively performed in real-time to induce specific affective states, based on the detection of affective state in a human listener. Machine learning algorithms will learn: (1) how to use biosensors such as EEG to detect the user’s current emotional state; and (2) how to use algorithmic performance and composition to induce certain trajectories through affective states. In other words the system will attempt to adapt so that it can – in real-time - turn a certain user from depressed to happy, or from stressed to relaxed, or (if they like horror movies!) from relaxed to fearful. Expressive performance is key to this process as it has been shown to increase the emotional impact of affectively-based algorithmic composition. In other words if a piece is composed by computer rules to communicate an emotion of happiness, applying expressive performance rules to humanize the piece will increase the likelihood it is perceived as happy. As well as giving a project overview, a first step of this research is presented here: a machine learning system using case-based reasoning which attempts to learn from a user how themes of different affective types combine sequentially to communicate emotions.
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Kirke, A., Miranda, E.R., Nasuto, S.J. (2013). Learning to Make Feelings: Expressive Performance as a Part of a Machine Learning Tool for Sound-Based Emotion Control. In: Aramaki, M., Barthet, M., Kronland-Martinet, R., Ystad, S. (eds) From Sounds to Music and Emotions. CMMR 2012. Lecture Notes in Computer Science, vol 7900. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41248-6_29
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