  The present methodology is aimed at cross-modal machine learning and uses
multidisciplinary tools and methods drawn from a broad range of areas and
disciplines, including music, systematic musicology, dance, motion capture,
human-computer interaction, computational linguistics and audio signal
processing. Main tasks include: (1) adapting wisdom-of-the-crowd approaches to
embodiment in music and dance performance to create a dataset of music and
music lyrics that covers a variety of emotions, (2) applying
audio/language-informed machine learning techniques to that dataset to identify
automatically the emotional content of the music and the lyrics, and (3)
integrating motion capture data from a Vicon system and dancers performing on
that music.
