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ISMIR
2005
Springer

Markov Random Fields and Maximum Entropy Modeling for Music Information Retrieval

13 years 9 months ago
Markov Random Fields and Maximum Entropy Modeling for Music Information Retrieval
Music information retrieval is characterized by a number of various user information needs. Systems are being developed that allow searchers to find melodies, rhythms, genres, and singers or artists, to name but a few. At the heart of all these systems is the need to find models or measures that answer the question “how similar are two given pieces of music”. However, similarity has a variety of meanings depending on the nature of the system being developed. More importantly, the features extracted from a music source are often either single-dimensional (i.e.: only pitch, or only rhythm, or only timbre) or else assumed to be orthogonal. In this paper we present a framework for developing systems which combine a wide variety of non-independent features without having to make the independence assumption. As evidence of effectiveness, we evaluate the system on the polyphonic theme similarity task over symbolic data. Nevertheless, we emphasize that the framework is general, and can ...
Jeremy Pickens, Costas S. Iliopoulos
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ISMIR
Authors Jeremy Pickens, Costas S. Iliopoulos
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