Enabling Embodied Analogies in Intelligent Music Systems

Fabio Paolizzo

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arXiv · 1712.00334

Enabling Embodied Analogies in Intelligent Music Systems

Fabio Paolizzo

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Abstract

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.

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Fabio Paolizzo. 2017-11-30. Enabling Embodied Analogies in Intelligent Music Systems. https://arxiv.org/abs/1712.00334

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