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—We propose an automatic method for measuring content based music similarity, enhancing the current generation of music search engines and recommender systems. Many previous approaches to track similarity require brute-force, pair-wise processing between all audio features in a database and therefore are not practical for large collections. However, in an(More)
We introduce a new model for extracting end points of music structure segments, such as intro, verse, chorus, break and so forth, from recorded music. Our methods are applied to the problem of grouping audio features into continuous structural segments with start and end times corresponding as closely as possible to a ground truth of independent human(More)
Modern collections of symbolic and audio music content provide unprecedented possibilities for musicological research , but traditional qualitative evaluation methods cannot realistically cope with such amounts of data. We are interested in harmonic analysis and propose key-independent chord idioms derived from a bottom-up analysis of musical data as a new(More)
We investigate explicit segment duration models in addressing the problem of fragmentation in musical audio segmentation. The resulting probabilistic models are optimised using Markov Chain Monte Carlo methods; in particular, we introduce a modification to Wolff's algorithm to make it applicable to a segment classification model with an arbitrary duration(More)
We describe an algorithm for finding approximate sequence similarity at all scales of interest, being explicit about our modelling assumptions and the parameters of the algorithm. We further present an algorithm for producing section labels based on the sequence similarity, and compare these labels with some expert-provided ground truth for a particular set(More)