Anchor space for classification and similarity measurement of music

This paper describes a method of mapping music into a semantic space that can be used for similarity measurement, classification, and music information retrieval. The value along each dimension of this anchor space is computed as the output from a pattern classifier which is trained to measure a particular semantic feature. In anchor space, distributions that represent objects such as artists or songs are modeled with Gaussian Mixture Models, and several similarity measures are defined by computing approximations to the Kullback-Leibler divergence between distributions. Similarity measures are evaluated against human similarity judgements. The models are also used for artist classification to achieve 62% accuracy on a 25-artist set, and 38% on a 404-artist set (random guessing achieves 0.25%). Finally, we describe a music similarity browsing application that makes use of the fact that anchor space dimensions are meaningful to users.