Retrieval and Annotation of Music Using Latent Semantic Models

Abstract

This thesis investigates the use of latent semantic models for annotation and retrieval from collections of musical audio tracks. In particular latent semantic analysis (LSA) and aspect models (or probabilistic latent semantic analysis, pLSA) are used to index words in descriptions of music drawn from hundreds of thousands of social tags. A new discrete audio feature representation is introduced to encode musical characteristics of automatically-identified regions of interest within each track, using a vocabulary of audio muswords. Finally a joint aspect model is developed that can learn from both tagged and untagged tracks by indexing both conventional words and muswords. This model is used as the basis of a music search system that supports query by example and by keyword, and of a simple probabilistic machine annotation system. The models are evaluated by their performance in a variety of realistic retrieval and annotation tasks, motivated by applications including playlist generation, internet radio streaming, music recommendation and catalogue search