Query-by-semantic-description (QBSD)is a natural paradigm for retrieving content from large databases of music. A major impediment to the development of good QBSD systems for music information retrieval has been the lack of a cleanly-labeled, publicly-available, heterogeneous data set of songs and associated annotations. We have collected the Computer Audition Lab 500-song (CAL500) data set by having humans listen to and annotate songs using a survey designed to capture 'semantic associations' between music and words. We adapt the supervised multi-class labeling (SML) model, which has shown good performance on the task of image retrieval, and use the CAL500 data to learn a model for music retrieval. The model parameters are estimated using the  weighted mixture hierarchies expectation-maximization  algorithm which has been specifically designed to handle real-valued semantic association between words and songs, rather than binary class labels. The output of the SML model, a vector of class-conditional probabilities, can be interpreted as a  semantic multinomial  distribution over a vocabulary. By also representing a semantic query as a  query multinomial  distribution, we can quickly rank order the songs in a database based on the Kullback-Leibler divergence between the query multinomial and each song's semantic multinomial. Qualitative and quantitative results demonstrate that our SML model can both annotate a novel song with meaningful words and retrieve relevant songs given a multi-word, text-based query.
['Douglas Turnbull', 'Luke Barrington', 'David A. Torres', 'Gert R. G. Lanckriet']
Towards musical query-by-semantic-description using the CAL500 data set