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AERFAI Summer School on Deep Learning


Alicante July 5-6, 2017

10th international workshop on Machine Learning and Music


Barcelona: 16th October 2017



  1. Pertusa, A.;Iñesta, J.M.
    "Efficient methods for joint estimation of multiple fundamental frequencies in music signals"
    EURASIP Journal on Advances in Signal Processing, vol. 2012, pp. 27 (2012)
    : bibtex : URL

    This study presents efficient techniques for multiple fundamental frequency estimation in music signals. The proposed methodology can infer harmonic patterns from a mixture considering interactions with other sources and evaluate them in a joint estimation scheme. For this purpose, a set of fundamental frequency candidates are first selected at each frame, and several hypothetical combinations of them are generated. Combinations are independently evaluated, and the most likely is selected taking into account the intensity and spectral smoothness of its inferred patterns. The method is extended considering adjacent frames in order to smooth the detection in time, and a pitch tracking stage is finally performed to increase the temporal coherence. The proposed algorithms were evaluated in MIREX contests yielding state of the art results with a very low computational burden.

@article {
 author = "Pertusa, A.;Iñesta, J.M.",
 title  = "Efficient methods for joint estimation of multiple fundamental frequencies in music signals",
 issn = "1687-6180",
 journal = "EURASIP Journal on Advances in Signal Processing",
 number = "1",
 pages = "27",
 volume = "2012",
 year = "2012"
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