@MISC\{IMM2005-04010,
    author       = "P. Ahrendt and A. Meng",
    title        = "Music Genre Classification using the multivariate {AR} feature integration model",
    year         = "2005",
    month        = "aug",
    keywords     = "Multivariate Autoregressive Model, Music Genre Classification",
    publisher    = "",
    address      = "",
    note         = "Extended Abstract",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4010-full.html",
    abstract     = "Music genre classification systems are normally build as
a feature extraction module followed by a classifier. The
features are often short-time features with time frames of
10-30ms, although several characteristics of music require
larger time scales. Thus, larger time frames are needed to
take informative decisions about musical genre. For the
{MIREX} music genre contest several authors derive long
time features based either on statistical moments and/or
temporal structure in the short time features. In our contribution
we model a segment (1.2 s) of short time features
(texture) using a multivariate autoregressive model. Other
authors have applied simpler statistical models such as the
mean-variance model, which also has been included in
several of this years {MIREX} submissions, see e.g. Tzanetakis
(2005); Burred (2005); Bergstra et al. (2005); Lidy
and Rauber (2005)."
}