Finding Motifs in Psychophysiological Responses and Chord Sequences

Annotation of emotion in music has traditionally used human tagging approaches. We propose a novel approach of identifying important musical features that can lead to automatic emotion annotation for music. Using psychophysiological responses of a subject listening to music and chord sequences of the songs, we identify music segments that can be used to describe the emotion that the music induces. An algorithm is then used to discover motifs – a pair of very similar subsequences in the data. These motifs are paired with chord progressions that are found to coincide with the physiological signal motifs. Results show that some of the identified chord progressions frequently appear in the music. Some of these chord progressions are frequently used in popular music. Using techniques developed, a library of chord sequences that induce a specific set of psychophysiological responses can be built for a music recommendation system.