@CONFERENCE\{IMM2004-02981,
    author       = "P. Ahrendt and A. Meng and J. Larsen",
    title        = "Decision Time Horizon for Music Genre Classification using Short Time Features",
    year         = "2004",
    month        = "sep",
    keywords     = "music genre classification, decision time horizon, feature ranking, dynamic {PCA,} majority voting",
    pages        = "1293--1296",
    booktitle    = "EUSIPCO",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "Vienna, Austria",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/2981-full.html",
    abstract     = "In this paper music genre classification has been explored with special emphasis on the decision time horizon and ranking of tapped-delay-line short-time features. Late information fusion as e.g. majority voting is compared with techniques of early information fusion such as dynamic {PCA} (DPCA). The most frequently suggested features in the literature were employed including mel-frequency cepstral coefficients (MFCC), linear prediction coefficients (LPC), zero-crossing rate (ZCR), and {MPEG-}7 features. To rank the importance of the short time features consensus sensitivity analysis is applied. A Gaussian classifier (GC) with full covariance structure and a linear neural network (NN) classifier are used."
}