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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)
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 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)
This paper presents a methodology for extracting meaningful audio/visual features from video streams. We propose a statistical method that does not distinguish between the auditory and visual data, but one that operates on a fused data set. By doing so we discover audio/visual features that correspond to events depicted in the stream. Using these features,(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)
• General audio consists of a wide range of sound phenomena such as music, sound effects, • Au: In TOC the chapter title is given as Sound Classification, please clarify. • Au: Please provide affiliation details. environmental sounds, speech and nonspeech utterances. The sound recognition tools provide a means for classifying and querying such diverse audio(More)