@CONFERENCE\{IMM2005-03309,
    author       = "A. Meng and P. Ahrendt and J. Larsen",
    title        = "Improving Music Genre Classification by Short-Time Feature Integration",
    year         = "2005",
    month        = "mar",
    keywords     = "Audio classification, Feature Integration, early/late Information fusion,",
    pages        = "497-500",
    booktitle    = "{IEEE} International Conference on Acoustics, Speech, and Signal Processing",
    volume       = "V",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/3309-full.html",
    abstract     = "Many different short-time features, using time windows in the size of 10-30 ms, have been proposed for music
segmentation, retrieval and genre classification. However, often the available time frame of the music to make the
actual decision or comparison (the decision time horizon) is in the range of seconds instead of milliseconds. The
problem of making new features on the larger time scale from the short-time features (feature integration) has
only received little attention. This paper investigates different methods for feature integration and late information
fusion for music genre classification. A new feature integration
technique, the {AR} model, is proposed and seemingly outperforms the commonly used mean-variance features."
}