Project

Learning the Meaning of Music

We aim to computationally model the meaning of music by taking advantage of community usage and description�using the self-selected and natural similarity clusters, opinions ,and usage patterns as labels and ground truth to inform on-line and unsupervised "music acquisition" systems that learn about music by listening and reading. We present a framework for capturing community metadata from free-text sources, audio representations robust enough to handle event and meaning relationships yet general enough to work across domains of music, and a machine-learning framework for learning the relationship between meaning and music automatically and iteratively from a cold start. These unbiased and organic machine-learning approaches show superior accuracy in music and multimedia intelligence tasks such as similarity, artist classification, and recommendation.

We aim to computationally model the meaning of music by taking advantage of community usage and description�using the self-selected and natural similarity clusters, opinions ,and usage patterns as labels and ground truth to inform on-line and unsupervised "music acquisition" systems that learn about music by listening and reading. We present a framework for capturing community metadata from free-text sources, audio representations robust enough to handle event and meaning relationships yet general enough to work across domains of music, and a machine-learning framework for learning the relationship between meaning and music automatically and iteratively from a cold start. These unbiased and organic machine-learning approaches show superior accuracy in music and multimedia intelligence tasks such as similarity, artist classification, and recommendation.