@CONFERENCE\{IMM2015-06880,
    author       = "B. L. Sturm and C. Kereliuk and J. Larsen",
    title        = "?El Caballo Viejo? Latin Genre Recognition with Deep Learning and Spectral Periodicity",
    year         = "2015",
    month        = "jun",
    booktitle    = "Fifth Biennial International Conference on Mathematics and Computation in Music (MCM2015)",
    volume       = "",
    series       = "Lecture Notes in Computer Science",
    editor       = "",
    publisher    = "Springer",
    organization = "",
    address      = "",
    url          = "http://mcm2015.qmul.ac.uk/",
    abstract     = "The winning{''} system in the 2013 {MIREX} Latin Genre Classification Train-test Task was a deep neural network trained with simple periodicity features. The explanation for its winning performance has yet to be fully explained. In our previous work, we built similar systems using the {BALLROOM} music dataset, and found its performance to be greatly affected by minor time dilation of the input, in effect changing music tempo. In the {MIREX} task, however, systems are trained and tested using the Latin Music Dataset (LMD), which is 4.5 times larger than {BALLROOM} and which does not seem to show a strong relation-
ship between tempo and label. In this paper, we reproduce the {''}winning{''} deep learning system using {LMD,} and measure the effects of time dilation on its performance. We find that, just as for {BALLROOM,} tempo changes of at most 6\% greatly hurt or benefit its performance. Interpreted with the low-level nature of the input features, this supports the conclusion that the system is exploiting some low-level absolute time characteristics to reproduce the ground truth in {LMD}."
}