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Abstract: Data mining in large collections of polyphonic music has recently received increasing interest by companies along with the advent of commercial online distribution of music. Important applications include the categorization of songs into genres and the recommendation of songs according to musical similarity and the customer's musical preferences. Modeling genre or timbre of polyphonic music is at the core of these tasks and has been recognized as a di#cult problem. Many audio features have been ... (Update)
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BibTeX entry: (Update)
@misc{ generation-understandable,
author = "Exhaustive Feature Generation",
title = "Understandable Models Of Music Collections Based On",
url = "citeseer.ist.psu.edu/765546.html" }
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