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We propose the method of independent subspace analysis (ISA) for separating individual audio sources from a single-channel mixture. ISA is based on independent component analysis (ICA) but relaxes the constraint that requires at least as many mixture observation signals as sources. A second extension to ICA is the use of dynamic components to represent(More)
We propose an automatic method for measuring content-based music similarity, enhancing the current generation of music search engines and recommended 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)
We propose a method for automatic fine-scale audio description that draws inspiration from ontological sound description methods such as Shaeffer's Objets Sonores and Smalley's Spectromorphology. Our goal is complete automation of audio description at the level of sound objects for indexing and retrieving sound segments within Internet audio documents. To(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)
A method for segmenting musical audio with a hierarchical timbre model is introduced. New evidence is presented to show that music segmentation can be recast as clustering of timbre features, and a new clustering algorithm is described. A prototype thumbnail-generating application is described and evaluated. Experimental results are given, including(More)
This paper demonstrates the importance of temporal sequences for passage-level music information retrieval. A number of audio analysis problems are solved successfully by using models that throw away the temporal sequence data. This paper suggests that we do not have this luxury when we consider a more difficult problem: that is finding musically similar(More)