The affective remixer: personalized music arranging

This paper describes a real-time music-arranging system that reacts to immediate affective cues from a listener. Data was collected on the potential of certain musical dimensions to elicit change in a listener's affective state using sound files created explicitly for the experiment through composition/production, segmentation, and re-assembly of music along these dimensions. Based on listener data, a probabilistic state transition model was developed to infer the listener's current affective state. A second model was made that would select music segments and re-arrange (`remix') them to induce a target affective state. We propose that this approach provides a new perspective for characterizing musical preference. Author Keywords Music, Affective computing, Personal soundtracks, Automated music arranging, Automated remixing, Predictive listening, Affective-state-transition model ACM Classification Keywords H.5.1 Multimedia Information Systems; H5.5 [Sound and music comp...

• Type: Conference
• Year: 2006
• Where: CHI
• Authors: Jae-woo Chung, G. Scott Vercoe
• Keywords: Affective Cues, CHI 2006, Current Affective State, Human Computer Interaction, Target Affective State